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bo-graduation/nltk-book/pattern-master/pattern/vector/__init__.py

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#### PATTERN | VECTOR ##############################################################################
# -*- coding: utf-8 -*-
# Copyright (c) 2010 University of Antwerp, Belgium
# Author: Tom De Smedt <tom@organisms.be>
# License: BSD (see LICENSE.txt for details).
# http://www.clips.ua.ac.be/pages/pattern
####################################################################################################
# Vector space model, based on cosine similarity using tf-idf.
# Documents (e.g., a sentence or a text) are represented as bag-of-words:
# the unordered words in the document and their (relative frequency).
# The dictionary of word => frequency items is called the document vector.
# The frequency weight is either TF or TF-IDF (term frequency-inverse document frequency, i.e.,
# the relevance of a word in a document offset by the frequency of the word in all documents).
# Documents can be grouped in a Model to calculate TF-IDF and cosine similarity,
# which measures similarity (0.0-1.0) between documents based on the cosine distance metric.
# A document cay have a type (or label). A model of labeled documents can be used to train
# a classifier. A classifier can be used to predict the label of unlabeled documents.
# This is called supervised machine learning (since we provide labeled training examples).
# Unsupervised machine learning or clustering can be used to group unlabeled documents
# into subsets based on their similarity.
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import absolute_import
from __future__ import division
from builtins import str, bytes, dict, int
from builtins import map, zip, filter
from builtins import object, range, next
from collections import OrderedDict
from . import stemmer
_stemmer = stemmer
import sys
import os
import re
import glob
import heapq
import codecs
import tempfile
try:
# Python 2
import cPickle as pickle
except ImportError:
# Python 3
import pickle
import gzip
import types
from math import log, exp, sqrt, tanh
from time import time
from random import random, randint, uniform, choice, sample, seed
from itertools import chain
from bisect import insort
from operator import itemgetter
from collections import defaultdict
from io import open
import numpy as np
import scipy
try:
MODULE = os.path.dirname(os.path.realpath(__file__))
except:
MODULE = ""
try:
from pattern.text import singularize, predicative, conjugate, tokenize
except:
try:
import sys
sys.path.insert(0, os.path.join(MODULE, ".."))
from text import singularize, predicative, conjugate, tokenize
except:
singularize = lambda w, **k: w
predicative = lambda w, **k: w
conjugate = lambda w, t, **k: w
tokenize = lambda s: list(filter(len,
re.split(r"(.*?[\.|\?|\!])",
re.sub(r"(\.|\?|\!|,|;|:)", " \\1", s))))
from pattern.helpers import encode_string, decode_string
decode_utf8 = decode_string
encode_utf8 = encode_string
def shi(i, base="0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"):
""" Returns a short string hash for a given int.
"""
s = []
while i > 0:
i, r = divmod(i, len(base))
s.append(base[r])
return "".join(reversed(s))
#--- LIST FUNCTIONS --------------------------------------------------------------------------------
def shuffled(iterable, **kwargs):
""" Returns a copy of the given list with the items in random order.
"""
seed(kwargs.get("seed"))
return sorted(list(iterable), key=lambda x: random())
def chunk(iterable, n):
""" Returns an iterator of n successive equal-sized chunks from the given list.
"""
# list(chunk([1, 2, 3, 4], n=2)) => [[1, 2], [3, 4]]
a = list(iterable)
n = int(n)
i = 0
j = 0
for m in range(n):
j = i + len(a[m::n])
yield a[i:j]
i = j
def mix(iterables=[], n=10):
""" Returns an iterator that alternates the given lists, in n chunks.
"""
# list(mix([[1, 2, 3, 4], ["a", "b"]], n=2)) => [1, 2, "a", 3, 4, "b"]
a = [list(chunk(x, n)) for x in iterables]
for i in range(int(n)):
for x in a:
for item in x[i]:
yield item
def bin(iterable, key=lambda x: x, value=lambda x: x):
""" Returns a dictionary with items in the given list grouped by the given key.
"""
# bin([["a", 1], ["a", 2], ["b", 3]], key=lambda x: x[0]) =>
# {"a": [["a", 1], ["a", 2]],
# "b": [["b", 3]]
# }
m = defaultdict(list)
for x in iterable:
m[key(x)].append(value(x))
return m
def pimap(iterable, function, *args, **kwargs):
""" Returns an iterator of function(x, *args, **kwargs) for the iterable (x1, x2, x3, ...).
The function is applied in parallel over available CPU cores.
"""
from multiprocessing import Pool
global worker
def worker(x):
return function(x, *args, **kwargs)
return Pool(processes=None).imap(worker, iterable)
#--- READ-ONLY DICTIONARY --------------------------------------------------------------------------
class ReadOnlyError(Exception):
pass
class readonlyodict(OrderedDict):
def __init__(self, *args, **kwargs):
self._f = False
super(readonlyodict, self).__init__(*args, **kwargs)
self._f = True
@classmethod
def fromkeys(cls, k, default=None):
return readonlyodict((k, default) for k in k)
def __setitem__(self, *args, **kwargs):
if self._f:
raise ReadOnlyError
return super(readonlyodict, self).__setitem__(*args, **kwargs)
def __delitem__(self, *args, **kwargs):
if self._f:
raise ReadOnlyError
return super(readonlyodict, self).__delitem__(*args, **kwargs)
def pop(self, *args, **kwargs):
if self._f:
raise ReadOnlyError
return super(readonlyodict, self).pop(*args, **kwargs)
def popitem(self, *args, **kwargs):
if self._f:
raise ReadOnlyError
return super(readonlyodict, self).popitem(*args, **kwargs)
def update(self, *args, **kwargs):
if self._f:
raise ReadOnlyError
return super(readonlyodict, self).update(*args, **kwargs)
# Read-only dictionary, used for Document.terms and Document.vector
# (updating these directly invalidates the Document and Model cache).
class readonlydict(dict):
def __init__(self, *args, **kwargs):
dict.__init__(self, *args, **kwargs)
@classmethod
def fromkeys(cls, k, default=None):
return readonlydict((k, default) for k in k)
def copy(self):
return readonlydict(self)
def __setitem__(self, k, v):
raise ReadOnlyError
def __delitem__(self, k):
raise ReadOnlyError
def pop(self, k, default=None):
raise ReadOnlyError
def popitem(self, kv):
raise ReadOnlyError
def clear(self):
raise ReadOnlyError
def update(self, kv):
raise ReadOnlyError
def setdefault(self, k, default=None):
if k in self:
return self[k]
raise ReadOnlyError
# Read-only list, used for Model.documents.
class readonlylist(list):
def __init__(self, *args, **kwargs):
list.__init__(self, *args, **kwargs)
def __setitem__(self, i, v):
raise ReadOnlyError
def __delitem__(self, i):
raise ReadOnlyError
def append(self, v):
raise ReadOnlyError
def extend(self, v):
raise ReadOnlyError
def insert(self, i, v):
raise ReadOnlyError
def remove(self, v):
raise ReadOnlyError
def pop(self, i):
raise ReadOnlyError
#### DOCUMENT ######################################################################################
#--- STOP WORDS ------------------------------------------------------------------------------------
# A dictionary of (language, words)-items of function words, for example: {"en": {"the": True}}.
# - de: 950+, Marco Götze & Steffen Geyer
# - en: 550+, Martin Porter (http://snowball.tartarus.org)
# - es: 300+, Martin Porter
# - fr: 550+, Martin Porter, Audrey Baneyx
# - nl: 100+, Martin Porter, Damien van Holten
stopwords = _stopwords = {}
for f in glob.glob(os.path.join(MODULE, "stopwords-*.txt")):
language = os.path.basename(f)[-6:-4] # stopwords-[en].txt
w = codecs.open(f, encoding="utf-8")
w = (w.strip() for w in w.read().split(","))
stopwords[language] = dict.fromkeys(w, True)
# The following English words could also be meaningful nouns:
#from pattern.vector import stopwords
#for w in ["mine", "us", "will", "can", "may", "might"]:
# stopwords["en"].pop(w)
#--- WORD COUNT ------------------------------------------------------------------------------------
# Simple bag-of-word models are often made up of word frequencies or character trigram frequencies.
PUNCTUATION = ".,;:!?()[]{}`'\"@#$^&*+-|=~_"
def words(string, filter=lambda w: w.strip("'").isalnum(), punctuation=PUNCTUATION, **kwargs):
""" Returns a list of words (alphanumeric character sequences) from the given string.
Common punctuation marks are stripped from words.
"""
string = decode_utf8(string)
string = re.sub(r"([a-z|A-Z])'(m|s|ve|re|ll|d)", "\\1 <QUOTE/>\\2", string)
string = re.sub(r"(c|d|gl|j|l|m|n|s|t|un)'([a-z|A-Z])", "\\1<QUOTE/> \\2", string)
words = (w.strip(punctuation).replace("<QUOTE/>", "'", 1) for w in string.split())
words = (w for w in words if filter is None or filter(w) is not False)
words = [w for w in words if w]
return words
PORTER, LEMMA = "porter", "lemma"
def stem(word, stemmer=PORTER, **kwargs):
""" Returns the base form of the word when counting words in count().
With stemmer=PORTER, the Porter2 stemming algorithm is used.
With stemmer=LEMMA, either uses Word.lemma or inflect.singularize().
(with optional parameter language="en", pattern.en.inflect is used).
"""
if hasattr(word, "string") and stemmer in (PORTER, None):
word = word.string
if isinstance(word, str):
word = decode_utf8(word.lower())
if stemmer is None:
return word.lower()
if stemmer == PORTER:
return _stemmer.stem(word, **kwargs)
if stemmer == LEMMA:
if hasattr(word, "lemma"): # pattern.en.Word
w = word.string.lower()
if word.lemma is not None:
return word.lemma
if word.pos == "NNS":
return singularize(w)
if word.pos.startswith(("VB", "MD")):
return conjugate(w, "infinitive") or w
if word.pos.startswith(("JJ",)):
return predicative(w)
if word.pos.startswith(("DT", "PR", "WP")):
return singularize(w, pos=word.pos)
return w
return singularize(word, pos=kwargs.get("pos", "NN"))
if hasattr(stemmer, "__call__"):
return decode_utf8(stemmer(word))
return word.lower()
def count(words=[], top=None, threshold=0, stemmer=None, exclude=[], stopwords=False, language=None, **kwargs):
""" Returns a dictionary of (word, count)-items, in lowercase.
Words in the exclude list and stop words (by default, English) are not counted.
Words whose count falls below (or equals) the given threshold are excluded.
Words that are not in the given top most counted are excluded.
"""
# An optional dict-parameter can be used to specify a subclass of dict,
# e.g., count(words, dict=readonlydict) as used in Document.
count = kwargs.get("dict", dict)()
for w in words:
w1 = w
w2 = w
if hasattr(w, "string"): # pattern.en.Word
w1 = w.string.lower()
if isinstance(w, str):
w1 = w.lower()
w2 = w.lower()
if (stopwords or w1 not in _stopwords.get(language or "en", ())) and w1 not in exclude:
if stemmer is not None:
w2 = stem(w2, stemmer, **kwargs).lower()
dict.__setitem__(count, w2, (w2 in count) and count[w2] + 1 or 1)
for k in list(count.keys()):
if count[k] <= threshold:
dict.__delitem__(count, k)
if top is not None:
count = count.__class__(heapq.nsmallest(top, list(count.items()), key=lambda kv: (-kv[1], kv[0])))
return count
def character_ngrams(string="", n=3, top=None, threshold=0, exclude=[], **kwargs):
""" Returns a dictionary of (character n-gram, count)-items.
N-grams in the exclude list are not counted.
N-grams whose count falls below (or equals) the given threshold are excluded.
N-grams that are not in the given top most counted are excluded.
"""
# An optional dict-parameter can be used to specify a subclass of dict,
# e.g., count(words, dict=readonlydict) as used in Document.
count = defaultdict(int)
if n > 0:
for i in range(len(string) - n + 1):
w = string[i:i + n]
if w not in exclude:
count[w] += 1
if threshold > 0:
count = dict((k, v) for k, v in count.items() if v > threshold)
if top is not None:
count = dict(heapq.nsmallest(top, list(count.items()), key=lambda kv: (-kv[1], kv[0])))
return kwargs.get("dict", dict)(count)
chngrams = character_ngrams
#--- DOCUMENT --------------------------------------------------------------------------------------
# A Document is a bag of words in which each word is a feature.
# A Document is represented as a vector of weighted (TF-IDF) features.
# A Document can be part of a training model used for learning (i.e., clustering or classification).
_UID = 0
_SESSION = shi(int(time() * 1000)) # Avoid collision with pickled documents.
def _uid():
""" Returns a string id, for example: "NPIJYaS-1", "NPIJYaS-2", ...
The string part is based on the current time, the number suffix is auto-incremental.
"""
global _UID
_UID += 1
return _SESSION + "-" + str(_UID)
# Term relevance weight:
TF, TFIDF, TF_IDF, BINARY = \
"tf", "tf-idf", "tf-idf", "binary"
class Document(object):
# Document(string = "",
# filter = lambda w: w.lstrip("'").isalnum(),
# punctuation = PUNCTUATION,
# top = None,
# threshold = 0,
# stemmer = None,
# exclude = [],
# stopwords = False,
# name = None,
# type = None,
# language = None,
# description = None
# )
def __init__(self, string="", **kwargs):
""" An unordered bag-of-words representation of the given string, list, dict or Sentence.
Lists can contain tuples (of), strings or numbers.
Dicts can contain tuples (of), strings or numbers as keys, and floats as values.
Document.words stores a dict of (word, count)-items.
Document.vector stores a dict of (word, weight)-items,
where weight is the term frequency normalized (0.0-1.0) to remove document length bias.
Punctuation marks are stripped from the words.
Stop words in the exclude list are excluded from the document.
Only top words whose count exceeds the threshold are included in the document.
"""
kwargs.setdefault("filter", lambda w: w.lstrip("'").isalnum())
kwargs.setdefault("threshold", 0)
kwargs.setdefault("dict", readonlydict)
# A string of words: map to read-only dict of (word, count)-items.
if string is None:
w = kwargs["dict"]()
v = None
elif isinstance(string, str):
w = words(string, **kwargs)
w = count(w, **kwargs)
v = None
# A list of words: map to read-only dict of (word, count)-items.
elif isinstance(string, (list, tuple)) and not string.__class__.__name__ == "Text":
w = string
w = count(w, **kwargs)
v = None
# A set of unique words: map to ready-only dict of (word, 1)-items.
elif isinstance(string, set):
w = string
w = kwargs["dict"].fromkeys(w, 1)
v = None
# A Vector of (word, weight)-items: copy as document vector.
elif isinstance(string, Vector):
w = string
w = kwargs["dict"](w)
v = Vector(w)
# A dict of (word, count)-items: make read-only.
elif isinstance(string, dict):
w = string
w = kwargs["dict"](w)
v = None
# pattern.en.Sentence with Word objects: can use stemmer=LEMMA.
elif string.__class__.__name__ == "Sentence":
w = string.words
w = [w for w in w if kwargs["filter"](w.string)]
w = count(w, **kwargs)
v = None
# pattern.en.Text with Sentence objects, can use stemmer=LEMMA.
elif string.__class__.__name__ == "Text":
w = []
[w.extend(sentence.words) for sentence in string]
w = [w for w in w if kwargs["filter"](w.string)]
w = count(w, **kwargs)
v = None
# Another Document: copy words, wordcount, name and type.
elif isinstance(string, Document):
for k in ("name", "type", "label", "language", "description"):
if hasattr(string, k):
kwargs.setdefault(k, getattr(string, k))
w = string.terms
w = kwargs["dict"](w)
v = None
else:
raise TypeError("document string is not str, unicode, list, dict, Vector, Sentence or Text.")
self._id = _uid() # Document ID, used when comparing objects.
self._name = kwargs.get("name") # Name that describes the document content.
self._type = kwargs.get("type", # Type that describes the category or class of the document.
kwargs.get("label"))
self._language = kwargs.get("language")
self._description = kwargs.get("description", "")
self._terms = w # Dictionary of (word, count)-items.
self._vector = v # Cached tf-idf vector.
self._count = None # Total number of words (minus stop words).
self._model = None # Parent Model.
@classmethod
def load(cls, path):
""" Returns a new Document from the given text file path.
The given text file must be generated with Document.save().
"""
# Open unicode file.
s = open(path, "rb").read()
s = s.lstrip(codecs.BOM_UTF8)
s = decode_utf8(s)
a = {}
v = {}
# Parse document name and type.
# Parse document terms and frequency.
for s in s.splitlines():
if s.startswith("#"): # comment
a["description"] = a.get("description", "") + s.lstrip("#").strip() + "\n"
elif s.startswith("@name:"):
a["name"] = s[len("@name:") + 1:].replace("\\n", "\n")
elif s.startswith("@type:"):
a["type"] = s[len("@type:") + 1:].replace("\\n", "\n")
elif s.startswith("@language:"):
a["lang"] = s[len("@lang:") + 1:].replace("\\n", "\n")
else:
s = s.split(" ")
w, f = " ".join(s[:-1]), s[-1]
if f.isdigit():
v[w] = int(f)
else:
v[w] = float(f)
return cls(v, name = a.get("name"),
type = a.get("type"),
language = a.get("lang"),
description = a.get("description").rstrip("\n"))
def save(self, path):
""" Saves the document as a text file at the given path.
The file content has the following format:
# Cat document.
@name: cat
@type: animal
a 3
cat 2
catch 1
claw 1
...
"""
s = []
# Parse document description.
for x in self.description.split("\n"):
s.append("# %s" % x)
# Parse document name, type and language.
for k, v in (("@name:", self.name), ("@type:", self.type), ("@lang:", self.language)):
if v is not None:
s.append("%s %s" % (k, v.replace("\n", "\\n")))
# Parse document terms and frequency.
for w, f in sorted(self.terms.items()):
if isinstance(f, int):
s.append("%s %i" % (w, f))
if isinstance(f, float):
s.append("%s %.3f" % (w, f))
s = "\n".join(s)
s = encode_utf8(s)
# Save unicode file.
f = open(path, "wb")
f.write(codecs.BOM_UTF8)
f.write(s)
f.close()
def _get_model(self):
return self._model
def _set_model(self, model):
self._vector = None
self._model and self._model._update()
self._model = model
self._model and self._model._update()
model = corpus = property(_get_model, _set_model)
@property
def id(self):
return self._id
@property
def name(self):
return self._name
@property
def type(self):
return self._type
@property
def label(self):
return self._type
@property
def language(self):
return self._language
@property
def description(self):
return self._description
@property
def terms(self):
return self._terms
@property
def words(self):
return self._terms
@property
def features(self):
return list(self._terms.keys())
@property
def count(self):
# Yields the number of words in the document representation.
# Cache the word count so we can reuse it when calculating tf.
if not self._count:
self._count = sum(self.terms.values())
return self._count
@property
def wordcount(self):
return self._count
def __len__(self):
return len(self.terms)
def __iter__(self):
return iter(self.terms)
def __contains__(self, word):
return word in self.terms
def __getitem__(self, word):
return self.terms.__getitem__(word)
def get(self, word, default=None):
return self.terms.get(word, default)
def term_frequency(self, word):
""" Returns the term frequency of a given word in the document (0.0-1.0).
tf = number of occurences of the word / number of words in document.
The more occurences of the word, the higher its relative tf weight.
"""
return float(self.terms.get(word, 0)) / (self.count or 1)
tf = term_frequency
def term_frequency_inverse_document_frequency(self, word, weight=TFIDF):
""" Returns the word relevance as tf * idf (0.0-1.0).
The relevance is a measure of how frequent the word occurs in the document,
compared to its frequency in other documents in the model.
If the document is not incorporated in a model, simply returns tf weight.
"""
if self.model is not None and weight == TFIDF:
# Use tf if no model, or idf==None (happens when the word is not in the model).
idf = self.model.idf(word)
idf = idf is None and 1 or idf
return self.tf(word) * idf
return self.tf(word)
tf_idf = tfidf = term_frequency_inverse_document_frequency
def information_gain(self, word):
""" Returns the information gain for the given word (0.0-1.0).
"""
if self.model is not None:
return self.model.ig(word)
return 0.0
ig = infogain = information_gain
def gain_ratio(self, word):
""" Returns the information gain ratio for the given word (0.0-1.0).
"""
if self.model is not None:
return self.model.gr(word)
return 0.0
gr = gainratio = gain_ratio
@property
def vector(self):
""" Yields the document vector, a dictionary of (word, relevance)-items from the document.
The relevance is tf, tf * idf, infogain or binary if the document is part of a Model,
based on the value of Model.weight (TF, TFIDF, IG, GR, BINARY, None).
The document vector is used to calculate similarity between two documents,
for example in a clustering or classification algorithm.
"""
if not self._vector:
# See the Vector class below = a dict with extra functionality (copy, norm).
# When a document is added/deleted from a model, the cached vector is deleted.
w = getattr(self.model, "weight", TF)
if w not in (TF, TFIDF, IG, INFOGAIN, GR, GAINRATIO, BINARY):
f = lambda w: float(self._terms[w]); w=None
if w == BINARY:
f = lambda w: int(self._terms[w] > 0)
if w == TF:
f = self.tf
if w == TFIDF:
f = self.tf_idf
if w in (IG, INFOGAIN):
f = self.model.ig
if w in (GR, GAINRATIO):
f = self.model.gr
self._vector = Vector(((w, f(w)) for w in self.terms), weight=w)
return self._vector
@property
def concepts(self):
""" Yields the document concept vector if the document is part of an LSA model.
"""
return self.model and self.model.lsa and self.model.lsa.concepts.get(self.id) or None
def keywords(self, top=10, normalized=True):
""" Returns a sorted list of (relevance, word)-tuples that are top keywords in the document.
With normalized=True, weights are normalized between 0.0 and 1.0 (their sum will be 1.0).
"""
n = normalized and sum(self.vector.values()) or 1.0
v = ((f / n, w) for w, f in self.vector.items())
v = heapq.nsmallest(top, v, key=lambda v: (-v[0], v[1]))
return v
def cosine_similarity(self, document):
""" Returns the similarity between the two documents as a number between 0.0-1.0.
If both documents are part of the same model the calculations are cached for reuse.
"""
if self.model is not None:
return self.model.cosine_similarity(self, document)
if document.model is not None:
return document.model.cosine_similarity(self, document)
return cosine_similarity(self.vector, document.vector)
similarity = cosine_similarity
def copy(self):
d = Document(None, name=self.name, type=self.type, description=self.description)
dict.update(d.terms, self.terms)
return d
def __eq__(self, document):
return isinstance(document, Document) and self.id == document.id
def __ne__(self, document):
return not self.__eq__(document)
def __repr__(self):
return "Document(id=%s%s%s)" % (
repr(self._id),
self.name and ", name=%s" % repr(self.name) or "",
self.type and ", type=%s" % repr(self.type) or "")
# This is required because we overwrite the parent's __eq__() method.
# Otherwise objects will be unhashable in Python 3.
# More information: http://docs.python.org/3.6/reference/datamodel.html#object.__hash__
__hash__ = object.__hash__
Bag = BagOfWords = BOW = Document
#--- VECTOR ----------------------------------------------------------------------------------------
# A Vector represents document terms (called features) and their tf or tf * idf relevance weight.
# A Vector is a sparse represenation: i.e., a dictionary with only those features > 0.
# This is fast, usually also faster than LSA which creates a full vector space with non-zero values.
# Document vectors can be used to calculate similarity between documents,
# for example in a clustering or classification algorithm.
# To find the average feature length in a model:
# sum(len(d.vector) for d in model.documents) / float(len(model))
class Vector(readonlydict):
id = 0
def __init__(self, *args, **kwargs):
""" A dictionary of (feature, weight)-items of the features (terms, words) in a Document.
A vector can be used to compare the document to another document with a distance metric.
For example, vectors with 2 features (x, y) can be compared using 2D Euclidean distance.
Vectors that represent text documents can be compared using cosine similarity.
"""
s = kwargs.pop("sparse", True)
f = ()
w = None
if len(args) > 0:
# From a Vector (copy weighting scheme).
if isinstance(args[0], Vector):
w = args[0].weight
# From a dict.
if isinstance(args[0], dict):
f = list(args[0].items())
# From an iterator.
elif hasattr(args[0], "__iter__"):
f = iter(args[0])
Vector.id += 1
self.id = Vector.id # Unique ID.
self.weight = kwargs.pop("weight", w) # TF, TFIDF, IG, BINARY or None.
self._norm = None # Cached L2-norm.
# Exclude zero weights (sparse=True).
f = chain(f, list(kwargs.items()))
f = ((k, v) for k, v in f if not s or v != 0)
readonlydict.__init__(self, f)
@classmethod
def fromkeys(cls, k, default=None, **kwargs):
return Vector(((k, default) for k in k), **kwargs)
@property
def features(self):
return list(self.keys())
@property
def l2_norm(self):
""" Yields the Frobenius matrix norm (cached).
n = the square root of the sum of the absolute squares of the values.
The matrix norm is used to normalize (0.0-1.0) cosine similarity between documents.
"""
if self._norm is None:
self._norm = sum(w * w for w in self.values()) ** 0.5
return self._norm
norm = l2 = L2 = L2norm = l2norm = L2_norm = l2_norm
def copy(self):
return Vector(self, weight=self.weight, sparse=False)
def __call__(self, vector={}):
""" Vector(vector) returns a new vector updated with values from the given vector.
No new features are added. For example: Vector({1:1, 2:2})({1:0, 3:3}) => {1:0, 2:2}.
"""
if isinstance(vector, (Document, Model)):
vector = vector.vector
v = self.copy()
s = dict.__setitem__
for f, w in vector.items():
if f in v:
s(v, f, w)
return v
#--- VECTOR DISTANCE -------------------------------------------------------------------------------
# The "distance" between two vectors can be calculated using different metrics.
# For vectors that represent text, cosine similarity is a good metric.
# For more information, see Domain Similarity Measures (Vincent Van Asch, 2012).
# The following functions can be used if you work with Vectors or plain dictionaries,
# instead of Documents and Models (which use caching for cosine similarity).
def features(vectors=[]):
""" Returns the set of unique features for all given vectors.
"""
return set(chain(*vectors))
_features = features
def sparse(v):
""" Returns the vector with features that have weight 0 removed.
"""
for f, w in list(v.items()):
if w == 0:
del v[f]
return v
def relative(v):
""" Returns the vector with feature weights normalized so that their sum is 1.0 (in-place).
"""
n = float(sum(v.values())) or 1.0
s = dict.__setitem__
for f in v: # Modified in-place.
s(v, f, v[f] / n)
return v
normalize = rel = relative
def l2_norm(v):
""" Returns the L2-norm of the given vector.
"""
if isinstance(v, Vector):
return v.l2_norm
return sum(w * w for w in v.values()) ** 0.5
norm = l2 = L2 = L2norm = l2norm = L2_norm = l2_norm
def cosine_similarity(v1, v2):
""" Returns the cosine similarity of the given vectors.
"""
s = sum(v1.get(f, 0) * w for f, w in v2.items())
s = float(s) / (l2_norm(v1) * l2_norm(v2) or 1)
return s
cos = cosine_similarity
def tf_idf(vectors=[], base=2.71828): # Euler's number
""" Calculates tf * idf on the vector feature weights (in-place).
"""
df = {}
for v in vectors:
for f in v:
if v[f] != 0:
df[f] = df[f] + 1 if f in df else 1.0
n = len(vectors)
s = dict.__setitem__
for v in vectors:
for f in v: # Modified in-place.
s(v, f, v[f] * (log(n / df[f], base)))
return vectors
tfidf = tf_idf
COSINE, EUCLIDEAN, MANHATTAN, CHEBYSHEV, HAMMING = \
"cosine", "euclidean", "manhattan", "chebyshev", "hamming"
def distance(v1, v2, method=COSINE):
""" Returns the distance between two vectors.
"""
if method == COSINE:
return 1 - cosine_similarity(v1, v2)
if method == EUCLIDEAN: # Squared Euclidean distance is used (1.5x faster).
return sum((v1.get(w, 0) - v2.get(w, 0)) ** 2 for w in set(chain(v1, v2)))
if method == MANHATTAN:
return sum(abs(v1.get(w, 0) - v2.get(w, 0)) for w in set(chain(v1, v2)))
if method == CHEBYSHEV:
return max(abs(v1.get(w, 0) - v2.get(w, 0)) for w in set(chain(v1, v2)))
if method == HAMMING:
d = sum(not (w in v1 and w in v2 and v1[w] == v2[w]) for w in set(chain(v1, v2)))
d = d / float(max(len(v1), len(v2)) or 1)
return d
if isinstance(method, type(distance)):
# Given method is a function of the form: distance(v1, v2) => float.
return method(v1, v2)
_distance = distance
def entropy(p=[], base=None):
""" Returns the Shannon entropy for the given list of probabilities
as a value between 0.0-1.0, where higher values indicate uncertainty.
"""
# entropy([1.0]) => 0.0, one possible outcome with a 100% chance
# entropy([0.5, 0.5]) => 1.0, two outcomes with a 50% chance each (random).
p = list(p)
s = float(sum(p)) or 1.0
s = s if len(p) > 1 else max(s, 1.0)
b = base or max(len(p), 2)
return -sum(x / s * log(x / s, b) for x in p if x != 0) or 0.0
#### MODEL #########################################################################################
#--- MODEL -----------------------------------------------------------------------------------------
# A Model is a representation of a collection of documents as bag-of-words.
# A Model is a matrix (or vector space) with features as columns and documents as rows,
# where each document is a vector of features (e.g., words) and feature weights (e.g., frequency).
# The matrix is used to calculate adjusted weights (e.g., tf * idf), document similarity and LSA.
# Export formats:
ORANGE, WEKA = "orange", "weka"
# LSA reduction methods:
NORM, L1, L2, TOP300 = "norm", "L1", "L2", "top300"
# Feature selection methods:
INFOGAIN, GAINRATIO, CHISQUARE, CHISQUARED = "infogain", "gainratio", "chisquare", "chisquared"
IG, GR, X2, DF = "ig", "gr", "x2", "df"
# Clustering methods:
KMEANS, HIERARCHICAL = "k-means", "hierarchical"
# Resampling methods:
MINORITY, MAJORITY = "minority", "majority"
class Model(object):
def __init__(self, documents=[], weight=TFIDF):
""" A model is a bag-of-word representation of a corpus of documents,
where each document vector is a bag of (word, relevance)-items.
Vectors can then be compared for similarity using a distance metric.
The weighting scheme can be: relative TF, TFIDF (default), IG, BINARY, None,
where None means that the original weights are used.
"""
self.description = "" # Description of the dataset: author e-mail, etc.
self._documents = readonlylist() # List of documents (read-only).
self._index = {} # Document.name => Document.
self._df = {} # Cache of document frequency per word.
self._cos = {} # Cache of ((d1.id, d2.id), relevance)-items (cosine similarity).
self._pp = {} # Cache of ((word, type), probability)-items.
self._x2 = {} # Cache of (word, chi-squared p-value)-items.
self._ig = {} # Cache of (word, information gain)-items.
self._gr = {} # Cache of (word, information gain ratio)-items.
self._inverted = {} # Cache of word => Document.
self._vector = None # Cache of model vector with all the features in the model.
self._classifier = None # Classifier trained on the documents in the model (NB, KNN, SVM).
self._lsa = None # LSA matrix with reduced dimensionality.
self._weight = weight # Weight used in Document.vector (TF, TFIDF, IG, BINARY or None).
self._update()
self.extend(documents)
@property
def documents(self):
return self._documents
docs = documents
@property
def terms(self):
return list(self.vector.keys())
features = words = terms
@property
def classes(self):
return list(set(d.type for d in self.documents))
labels = classes
@property
def classifier(self):
return self._classifier
@property
def distribution(self):
p = defaultdict(int)
for d in self.documents:
p[d.type] += 1
return p
def _get_lsa(self):
return self._lsa
def _set_lsa(self, v=None):
self._update() # Clear the cache.
self._lsa = v
lsa = property(_get_lsa, _set_lsa)
def _get_weight(self):
return self._weight
def _set_weight(self, w):
self._update() # Clear the cache.
self._weight = w
weight = property(_get_weight, _set_weight)
@classmethod
def load(cls, path):
""" Loads the model from a gzipped pickle file created with Model.save().
"""
model = pickle.loads(gzip.GzipFile(path, "rb").read())
# Deserialize Model.classifier.
if model.classifier:
p = path + ".tmp"
f = open(p, "wb")
f.write(model.classifier)
f.close()
model._classifier = Classifier.load(p)
os.remove(p)
return model
def save(self, path, update=False, final=False):
""" Saves the model as a gzipped pickle file at the given path.
The advantage is that cached vectors and cosine similarity are stored.
"""
# Update the cache before saving.
if update:
classes = self.classes
self.document_frequency("") # set self._df
self.inverted_index # set self._inverted
self.vector # set self._vector
self.posterior_probability("", "") # set self._pp
self.chi_squared("") # set self._x2
self.information_gain("") # set self._ig + self._gr
for d1 in self.documents: # set self._cos
for d2 in self.documents:
self.cosine_similarity(d1, d2)
# Serialize Model.classifier.
if self._classifier:
p = path + ".tmp"
self._classifier.save(p, final)
self._classifier = open(p, "rb").read()
os.remove(p)
f = gzip.GzipFile(path, "wb")
f.write(pickle.dumps(self, 1)) # 1 = binary
f.close()
def export(self, path, format=ORANGE, **kwargs):
""" Exports the model as a file for other machine learning applications,
e.g., Orange or Weka.
"""
# The Document.vector space is exported without cache or LSA concept space.
keys = sorted(self.vector.keys())
s = []
# Orange tab format:
if format.lower() == ORANGE:
s.append("\t".join(keys + ["m#name", "c#type"]))
for document in self.documents:
v = document.vector
v = [v.get(k, 0) for k in keys]
v = "\t".join(x == 0 and "0" or "%.4f" % x for x in v)
v = "%s\t%s\t%s" % (v, document.name or "", document.type or "")
s.append(v)
# Weka ARFF format:
if format.lower() == WEKA:
s.append("@RELATION %s" % kwargs.get("name", hash(self)))
s.append("\n".join("@ATTRIBUTE %s NUMERIC" % k for k in keys))
s.append("@ATTRIBUTE class {%s}" % ",".join(set(d.type or "" for d in self.documents)))
s.append("@DATA")
for document in self.documents:
v = document.vector
v = [v.get(k, 0) for k in keys]
v = ",".join(x == 0 and "0" or "%.4f" % x for x in v)
v = "%s,%s" % (v, document.type or "")
s.append(v)
s = "\n".join(s)
f = open(path, "w", encoding="utf-8")
f.write(decode_utf8(s))
f.close()
def _update(self):
# Ensures that all document vectors are recalculated
# when a document is added or deleted (= new features).
self._df = {}
self._cos = {}
self._pp = {}
self._x2 = {}
self._ig = {}
self._gr = {}
self._inverted = {}
self._vector = None
self._classifier = None
self._lsa = None
for document in self.documents:
document._vector = None
def __len__(self):
return len(self.documents)
def __iter__(self):
return iter(self.documents)
def __getitem__(self, i):
return self.documents.__getitem__(i)
def __delitem__(self, i):
d = list.pop(self.documents, i)
d._model = None
self._index.pop(d.name, None)
self._update()
def clear(self):
self._documents = readonlylist()
self._update()
def append(self, document):
""" Appends the given Document to the model.
If Model.weight != TF, the cache of vectors and cosine similarity is cleared
(feature weights will be different now that there is a new document).
"""
if not isinstance(document, Document):
document = Document(document)
if document.name is not None:
self._index[document.name] = document
document._model = self
list.append(self.documents, document)
if self._weight not in (TF, BINARY, None):
self._update()
def extend(self, documents):
""" Extends the model with the given list of documents.
"""
documents = list(documents)
for i, document in enumerate(documents):
if not isinstance(document, Document):
documents[i] = Document(document)
if document.name is not None:
self._index[document.name] = document
document._model = self
list.extend(self.documents, documents)
if self._weight not in (TF, BINARY, None):
self._update()
def remove(self, document):
""" Removes the given Document from the model, and sets Document.model=None.
"""
self.__delitem__(self.documents.index(document))
def document(self, name):
""" Returns the Document with the given name (assuming document names are unique).
"""
if name in self._index:
return self._index[name]
doc = document
def keywords(self, top=10, normalized=True):
""" Returns a sorted list of (relevance, word)-tuples that are top keywords in the model.
With normalized=True, weights are normalized between 0.0 and 1.0 (their sum will be 1.0).
"""
self.df(None) # Populate document frequency cache.
n = normalized and sum(self._df.values()) or 1.0
v = ((f / n, w) for w, f in self._df.items())
v = heapq.nsmallest(top, v, key=lambda v: (-v[0], v[1]))
return v
def document_frequency(self, word):
""" Returns the document frequency for the given word or feature.
Returns 0 if there are no documents in the model (e.g. no word frequency).
df = number of documents containing the word / number of documents.
The more occurences of the word across the model, the higher its df weight.
"""
if len(self.documents) == 0:
return 0.0
if len(self._df) == 0:
# Caching document frequency for each word gives a 300x performance boost
# (i.e., calculated all at once). Drawback is if you need it for just one word.
df = self._df
for d in self.documents:
for w, f in d.terms.items():
if f != 0:
df[w] = (w in df) and df[w] + 1 or 1.0
for w in df:
df[w] /= float(len(self.documents))
return self._df.get(word, 0.0)
df = document_frequency
def inverse_document_frequency(self, word, base=2.71828):
""" Returns the inverse document frequency for the given word or feature.
Returns None if the word is not in the model, or if there are no documents in the model.
Using the natural logarithm:
idf = log(1/df)
The more occurences of the word, the lower its idf weight (log() makes it grow slowly).
"""
df = self.df(word)
if df == 0.0:
return None
if df == 1.0:
return 0.0
return log(1.0 / df, base)
idf = inverse_document_frequency
@property
def inverted_index(self):
""" Yields a dictionary of (word, set([document1, document2, ...]))-items.
"""
if not self._inverted:
m = {}
for d in self.documents:
for w in d.terms:
if w not in m:
m[w] = set()
m[w].add(d)
self._inverted = m
return self._inverted
inverted = inverted_index
@property
def vector(self):
""" Returns a Vector dict of (word, 0.0)-items from the vector space model.
It includes all words from all documents (i.e. it is the dimension of the vector space).
Model.vector(document) yields a vector with the feature weights of the given document.
"""
# Notes:
# 1) Model.vector is the dictionary of all (word, 0.0)-items.
# 2) Model.vector(document) returns a copy with the document's word frequencies.
# This is the full vector, as opposed to the sparse Document.vector.
# Words in a document that are not in the model are ignored,
# i.e., the document was not in the model, this can be the case in Model.search().
# See: Vector.__call__().
if not self._vector:
self._vector = Vector(((w, 0.0) for w in chain(*(d.terms for d in self.documents))), sparse=False)
return self._vector
@property
def vectors(self):
""" Yields a list of all document vectors.
"""
return [d.vector for d in self.documents]
@property
def density(self):
""" Yields the overall word coverage as a number between 0.0-1.0.
"""
return float(sum(len(d.vector) for d in self.documents)) / len(self.vector) ** 2
# Following methods rely on Document.vector:
# frequent sets, cosine similarity, nearest neighbors, search, clustering,
# information gain, latent semantic analysis.
def frequent_concept_sets(self, threshold=0.5):
""" Returns a dictionary of (set(feature), frequency)
of feature combinations with a frequency above the given threshold.
"""
return apriori([d.terms for d in self.documents], support=threshold)
sets = frequent = frequent_concept_sets
def cosine_similarity(self, document1, document2):
""" Returns the similarity between two documents in the model as a number between 0.0-1.0,
based on the document feature weight (e.g., tf * idf of words in the text).
cos = dot(v1, v2) / (norm(v1) * norm(v2))
"""
# If we already calculated similarity between two given documents,
# it is available in cache for reuse.
id1 = document1.id
id2 = document2.id
if (id1, id2) in self._cos:
return self._cos[(id1, id2)]
if (id2, id1) in self._cos:
return self._cos[(id2, id1)]
# Calculate the matrix multiplication of the document vectors.
if not getattr(self, "lsa", None):
v1 = document1.vector
v2 = document2.vector
s = cosine_similarity(v1, v2)
else:
# Using LSA concept space:
v1 = id1 in self.lsa and self.lsa[id1] or self._lsa.transform(document1)
v2 = id2 in self.lsa and self.lsa[id2] or self._lsa.transform(document2)
s = cosine_similarity(v1, v2)
# Cache the similarity weight for reuse.
if document1.model == self and \
document2.model == self:
self._cos[(id1, id2)] = s
return s
similarity = cos = cosine_similarity
def nearest_neighbors(self, document, top=10):
""" Returns a list of (similarity, document)-tuples in the model,
sorted by cosine similarity to the given document.
"""
v = ((self.cosine_similarity(document, d), d) for d in self.documents)
# Filter the input document from the matches.
# Filter documents that score zero, and return the top.
v = [(w, d) for w, d in v if w > 0 and d.id != document.id]
v = heapq.nsmallest(top, v, key=lambda v: (-v[0], v[1]))
return v
similar = related = neighbors = nn = nearest_neighbors
def vector_space_search(self, words=[], **kwargs):
""" Returns related documents from the model as a list of (similarity, document)-tuples.
The given words can be a string (one word), a list or tuple of words, or a Document.
"""
top = kwargs.pop("top", 10)
if not isinstance(words, Document):
kwargs.setdefault("filter", lambda w: w) # pass-through.
kwargs.setdefault("stopwords", True)
words = Document(words)
if len([w for w in words if w in self.vector]) == 0:
return []
m, words._model = words._model, self # So we can calculate tf-idf.
n, words._model = self.nearest_neighbors(words, top), m
words._model = m
return n
search = vector_space_search
def distance(self, document1, document2, *args, **kwargs):
""" Returns the distance (COSINE, EUCLIDEAN, ...) between two document vectors (0.0-1.0).
"""
return distance(document1.vector, document2.vector, *args, **kwargs)
# def cluster(self, method=KMEANS, k=10, iterations=10)
# def cluster(self, method=HIERARCHICAL, k=1, iterations=1000)
def cluster(self, method=KMEANS, **kwargs):
""" Clustering is an unsupervised machine learning method for grouping similar documents.
- k-means clustering returns a list of k clusters (each is a list of documents).
- hierarchical clustering returns a list of documents and Cluster objects,
where a Cluster is a list of documents and other clusters (see Cluster.flatten()).
"""
# The optional documents parameter can be a selective list
# of documents in the model to cluster.
documents = kwargs.get("documents", self.documents)
if not getattr(self, "lsa", None):
# Using document vectors:
vectors, features = [d.vector for d in documents], list(self.vector.keys())
else:
# Using LSA concept space:
vectors, features = [self.lsa[d.id] for d in documents], list(range(len(self.lsa)))
# Create a dictionary of vector.id => Document.
# We need it to map the clustered vectors back to the actual documents.
map = dict((v.id, documents[i]) for i, v in enumerate(vectors))
if method in (KMEANS, "kmeans"):
clusters = k_means(vectors,
k = kwargs.pop("k", 10),
iterations = kwargs.pop("iterations", 10),
features = features, **kwargs)
if method == HIERARCHICAL:
clusters = hierarchical(vectors,
k = kwargs.pop("k", 1),
iterations = kwargs.pop("iterations", 1000),
features = features, **kwargs)
if method in (KMEANS, "kmeans"):
clusters = [[map[v.id] for v in cluster] for cluster in clusters]
if method == HIERARCHICAL:
clusters.traverse(visit=lambda cluster: \
[cluster.__setitem__(i, map[v.id])
for i, v in enumerate(cluster) if not isinstance(v, Cluster)])
return clusters
def latent_semantic_analysis(self, dimensions=NORM):
""" Creates LSA concept vectors by reducing the vector space's dimensionality.
Each concept vector has the given number of features (concepts).
The concept vectors are consequently used in Model.cosine_similarity(), Model.cluster()
and classification. This can be faster for high-dimensional vectors (i.e., many features).
The reduction can be undone by setting Model.lsa=False.
"""
self._lsa = LSA(self, k=dimensions)
self._cos = {}
return self._lsa
reduce = latent_semantic_analysis
def resample(self, n=MINORITY):
""" Modifies the model so that it contains n documents of each type.
With MINORITY, n = the number of documents in the minority class.
With MAJORITY, n = the number of documents in the majority class
(generates artificial documents for minority classes).
"""
# Based on:
# Liu, Ghosh & Martin (2007). Generative oversampling for mining imbalanced datasets.
# http://wwwmath.uni-muenster.de/u/lammers/EDU/ws07/Softcomputing/Literatur/4-DMI5467.pdf
#
# p1: {class: count}
# p2: {class: max(len(vector))}
# p3: {class: {feature: [weights]}}
p1 = defaultdict(int)
p2 = defaultdict(int)
p3 = defaultdict(lambda: defaultdict(list))
for d in self.documents:
p1[d.type] += 1
for d in self.documents:
p2[d.type] = max(p2[d.type], len(d.terms))
for d in self.documents:
for f, w in d.terms.items():
p3[d.type][f].append(w)
# List features with weight 0.0.
for t in p3:
for f in p3[t]:
p3[t][f].extend([0] * (p1[t] - len(p3[t][f])))
if n == MINORITY:
n = min(p1.values() or [0])
if n == MAJORITY:
n = max(p1.values() or [0])
a = []
x = defaultdict(int)
# 1) Undersampling.
for d in self.documents:
if x[d.type] < n:
x[d.type] += 1
a.append(d)
# 2) Oversampling.
# Generate new documents in minority classes,
# taking into account the number of features
# and feature weights in existing documents.
for t, i in p1.items():
for j in range(i, n):
v = {}
for f in shuffled(p3[t].keys()[:p2[t]]):
w = choice(p3[t][f])
w = w if v else p3[t][f][0]
if w:
v[f] = w
a.append(Document(v))
self.clear()
self.extend(a)
return self
def condensed_nearest_neighbor(self, k=1, distance=COSINE):
""" Returns a filtered list of documents, without impairing classification accuracy.
Iteratively constructs a set of "prototype" documents.
Documents that are correctly classified by the set are discarded.
Documents that are incorrectly classified by the set are added to the set.
"""
d = DistanceMap(method=distance)
u = []
v = list(self.documents)
b = False
while not b:
b = True
for i, x in enumerate(v):
nn = heapq.nsmallest(k, ((d(x.vector, y.vector), y) for y in u))
if not u or x.type in (y.type for d, y in nn):
b = False
u.append(x)
v.pop(i)
break
return v
cnn = condensed_nearest_neighbor
def posterior_probability(self, word, type):
""" Returns the probability that a document with the given word is of the given type.
"""
if not self._pp:
# p1: {class: count}
# p2: {feature: {class: count}}
# p3: {feature: count}
# p4: {(feature, class): probability}
p1 = defaultdict(float)
p2 = defaultdict(lambda: defaultdict(float))
p3 = defaultdict(float)
p4 = defaultdict(float)
for d in self.documents:
p1[d.type] += 1
for d in self.documents:
for f in d.terms:
p2[f][d.type] += 1 / p1[d.type]
p3[f] += 1
for t in p1:
for f in p3:
p4[(f, t)] = p1[t] * p2[f][t] / p3[f]
self._pp = p4
return self._pp[(word, type)]
pp = probability = posterior_probability
def chi_squared(self, word):
""" Returns the chi-squared p-value (0.0-1.0) for the given feature.
When p < 0.05, the feature is biased to a class (document type),
i.e., it is a significant predictor for that class.
"""
if not self._x2:
from pattern.metrics import chi2
# p1: {class: count}
# p2: {class: {feature: count}}
# p3: {feature: count}
# p4: {feature: p-value}
p1 = defaultdict(float)
p2 = defaultdict(lambda: defaultdict(float))
p3 = defaultdict(float)
p4 = defaultdict(float)
for d in self.documents:
p1[d.type] += 1
for d in self.documents:
for f in d.terms:
p2[d.type][f] += 1
p3[f] += 1
for f in p3:
p4[f] = chi2(observed=[[p2[t][f] for t in p2], [p1[t] - p2[t][f] for t in p2]])[1]
self._x2 = p4
return self._x2[word]
X2 = x2 = chi2 = chi_square = chi_squared
def information_gain(self, word):
""" Returns the information gain (IG, 0.0-1.0) for the given feature,
by measuring how much the feature contributes to each document type (class).
High information gain means low entropy. Low entropy means predictability,
i.e., a feature that is biased towards some class(es),
i.e., a feature that occurs more in one document type and less in others.
"""
if not self._ig:
# Based on Vincent Van Asch, http://www.clips.ua.ac.be/~vincent/scripts/textgain.py
# IG(f) = H(C) - sum(p(v) * H(C|v) for v in V)
# where C is the set of class labels,
# where V is the set of values for feature f,
# where p(v) is the probability that feature f has value v,
# where C|v is the distribution of value v for feature f per class.
# H is the entropy for a list of probabilities.
# Lower entropy indicates predictability, i.e., some values are more probable.
# H([0.50, 0.50]) = 1.00
# H([0.75, 0.25]) = 0.81
H = entropy
# C => {class: count}
C = dict.fromkeys(self.classes, 0)
for d in self.documents:
C[d.type] += 1
HC = H(list(C.values()))
# V => {feature: {value: {class: count}}}
F = set(self.features)
V = dict((f, defaultdict(lambda: defaultdict(lambda: 0))) for f in F)
for d in self.documents:
if self.weight in (IG, GR, INFOGAIN, GAINRATIO):
d_vector = dict.fromkeys(d.terms, True)
else:
d_vector = d.vector
# Count features by value per class.
# Equal-width binning.
# Features with float values are taken to range between 0.0-1.0,
# for which 10 discrete intervals are used (0.1, 0.2, 0.3, ...).
for f, v in d_vector.items():
if isinstance(v, float):
v = round(v, 1)
V[f][v][d.type] += 1
#for f in F - set(d_vector):
# V[f][0][type] += 1
# We also need to count features with value 0.0.
# This is done with the two lines above, however
# the code below is over a 1000x faster (less dict.__getitem__).
for f in F:
for type, n in C.items():
V[f][0][type] += n - sum(V[f][v][type] for v in V[f])
# IG
for f in F:
Vf = V[f]
n = sum(sum(Vf[v].values()) for v in Vf) # total value count
n = float(n) or 1
ig = HC
si = 0 # split info
for Cv in Vf.values():
Cv = list(Cv.values())
pv = sum(Cv) / n
ig = ig - pv * H(Cv)
si = si + H([pv])
self._ig[f] = ig
self._gr[f] = ig / (si or 1)
return self._ig.get(word, 0.0)
IG = ig = infogain = gain = information_gain
def gain_ratio(self, word):
""" Returns the information gain ratio (GR, 0.0-1.0) for the given feature.
"""
if not self._gr:
self.ig(word)
return self._gr[word]
GR = gr = gainratio = gain_ratio
def feature_selection(self, top=100, method=CHISQUARED, threshold=0.0, weighted=False):
""" Returns a list with the most informative features (terms), using information gain.
This is a subset of Model.features that can be used to build a Classifier
that is faster (less features = less matrix columns) but still efficient.
The given document frequency threshold excludes features that occur in
less than the given percentage of documents (i.e., outliers).
"""
if method is None:
f = lambda w: 1.0
if method in (X2, CHISQUARE, CHISQUARED, "X2"):
f = lambda w: 1.0 - self.x2(w)
if method in (IG, INFOGAIN):
f = self.ig
if method in (GR, GAINRATIO):
f = self.gr
if method == DF:
f = self.df
if hasattr(method, "__call__"):
f = method
subset = ((f(w), w) for w in self.terms if self.df(w) >= threshold)
subset = sorted(subset, key=itemgetter(1))
subset = sorted(subset, key=itemgetter(0), reverse=True)
subset = subset[:top if top is not None else len(subset)]
subset = subset if weighted else [w for x, w in subset]
return subset
def filter(self, features=[], documents=[]):
""" Returns a new Model with documents only containing the given list of features,
for example a subset returned from Model.feature_selection().
"""
documents = documents or self.documents
features = set(features)
model = Model(weight=self.weight)
model.extend([
Document(dict((w, f) for w, f in d.terms.items() if w in features),
name = d.name,
type = d.type,
language = d.language,
description = d.description) for d in documents])
return model
def train(self, *args, **kwargs):
""" Trains Model.classifier with the document vectors.
Each document is expected to have a Document.type.
Model.predict() can then be used to predict the type of other (unknown) documents.
"""
if len(args) == 0:
# Model.train(classifier=KNN)
Classifier = kwargs.pop("Classifier", NB)
if len(args) >= 1:
# Model.train(KNN, k=1)
Classifier = args[0]; args = args[1:]
kwargs["train"] = self
self._classifier = Classifier(*args, **kwargs)
self._classifier.finalize()
def predict(self, *args, **kwargs):
""" Returns the type for a given document,
based on the similarity of documents in the trained Model.classifier.
"""
return self._classifier.classify(*args, **kwargs)
# Backwards compatibility.
Corpus = Model
#### FREQUENT CONCEPT SETS #########################################################################
# Agrawal R. & Srikant R. (1994), Fast algorithms for mining association rules in large databases.
# Based on: https://gist.github.com/1423287
class Apriori(object):
def __init__(self):
self._candidates = []
self._support = {}
def C1(self, sets):
""" Returns the unique features from all sets as a list of (hashable) frozensets.
"""
return [frozenset([v]) for v in set(chain(*sets))]
def Ck(self, sets):
""" For the given sets of length k, returns combined candidate sets of length k+1.
"""
Ck = []
for i, s1 in enumerate(sets):
for j, s2 in enumerate(sets[i + 1:]):
if set(list(s1)[:-1]) == set(list(s2)[:-1]):
Ck.append(s1 | s2)
return Ck
def Lk(self, sets, candidates, support=0.0):
""" Prunes candidate sets whose frequency < support threshold.
Returns a dictionary of (candidate set, frequency)-items.
"""
Lk, x = {}, 1.0 / (len(sets) or 1) # relative count
for s1 in candidates:
for s2 in sets:
if s1.issubset(s2):
Lk[s1] = s1 in Lk and Lk[s1] + x or x
return dict((s, f) for s, f in Lk.items() if f >= support)
def __call__(self, sets=[], support=0.5):
""" Returns a dictionary of (set(features), frequency)-items.
The given support (0.0-1.0) is the relative amount of documents
in which a combination of features must appear.
"""
sets = [set(iterable) for iterable in sets]
C1 = self.C1(sets)
L1 = self.Lk(sets, C1, support)
self._candidates = [list(L1.keys())]
self._support = L1
while True:
# Terminate when no further extensions are found.
if len(self._candidates[-1]) == 0:
break
# Extend frequent subsets one item at a time.
Ck = self.Ck(self._candidates[-1])
Lk = self.Lk(sets, Ck, support)
self._candidates.append(list(Lk.keys()))
self._support.update(Lk)
return self._support
apriori = Apriori()
#### LATENT SEMANTIC ANALYSIS ######################################################################
# Based on:
# http://en.wikipedia.org/wiki/Latent_semantic_analysis
# http://blog.josephwilk.net/projects/latent-semantic-analysis-in-python.html
class LSA(object):
def __init__(self, model, k=NORM):
""" Latent Semantic Analysis is a statistical machine learning method based on
singular value decomposition (SVD), and related to principal component analysis (PCA).
Closely related features (words) in the model are combined into "concepts".
Documents then get a concept vector that is an approximation of the original vector,
but with reduced dimensionality so that cosine similarity and clustering run faster.
"""
# Calling Model.vector() in a loop is quite slow, we should refactor this:
matrix = [list(model.vector(d).values()) for d in model.documents]
matrix = np.array(matrix)
# Singular value decomposition, where u * sigma * vt = svd(matrix).
# Sigma is the diagonal matrix of singular values,
# u has document rows and concept columns, vt has concept rows and term columns.
u, sigma, vt = np.linalg.svd(matrix, full_matrices=False)
# Note: now np.dot(np.dot(u, np.diag(sigma)), vt) !≈ matrix
# assert np.allclose(np.dot(np.dot(u, np.diag(sigma)), vt), matrix)
# Delete the smallest coefficients in the diagonal matrix (i.e., at the end of the list).
# The difficulty and weakness of LSA is knowing how many dimensions to reduce
# (generally L2-norm is used).
if k == L1:
k = int(round(np.linalg.norm(sigma, 1)))
if k == L2 or k == NORM:
k = int(round(np.linalg.norm(sigma, 2)))
if k == TOP300:
k = max(0, len(sigma) - 300)
if isinstance(k, int):
k = max(0, len(sigma) - k)
if type(k).__name__ == "function":
k = max(0, int(k(sigma)))
#print(np.dot(u, np.dot(np.diag(sigma), vt)))
# Apply dimension reduction.
# The maximum length of a concept vector = the number of documents.
assert k < len(model.documents), \
"can't create more dimensions than there are documents"
tail = lambda x, i: list(range(len(x) - i, len(x)))
u, sigma, vt = (
np.delete(u, tail(u[0], k), axis=1),
np.delete(sigma, tail(sigma, k), axis=0),
np.delete(vt, tail(vt, k), axis=0)
)
# In some numpy versions, np.linalg.svd seems to yield negative components. SVD decomposition is not unique.
u, sigma, vt = list(map(np.abs, (u, sigma, vt)))
# Store as Python dict and lists so we can pickle it.
self.model = model
self._terms = dict(enumerate(model.vector().keys())) # Vt-index => word.
self.u, self.sigma, self.vt = (
dict((d.id, Vector((i, float(x)) for i, x in enumerate(v))) for d, v in zip(model, u)),
list(sigma),
[[float(x) for x in v] for v in vt]
)
@property
def terms(self):
""" Yields a list of all terms, identical to LSA.model.vector.keys().
"""
return list(self._terms.values())
features = words = terms
@property
def concepts(self):
""" Yields a list of all concepts, each a dictionary of (word, weight)-items.
"""
# Round the weight so 9.0649330400000009e-17 becomes a more meaningful 0.0.
return [dict((self._terms[i], round(w, 15)) for i, w in enumerate(concept)) for concept in self.vt]
@property
def vectors(self):
""" Yields a dictionary of (Document.id, concepts),
where each concept is a dictionary of (concept_index, weight)-items.
for document in lsa.model:
for concept in lsa.vectors(document.id):
print(document, concept)
"""
return self.u
def vector(self, id):
if isinstance(id, Document):
id = id.id
return self.u[id]
def __getitem__(self, id):
return self.u[id]
def __contains__(self, id):
return id in self.u
def __iter__(self):
return iter(self.u)
def __len__(self):
return len(self.u)
def transform(self, document):
""" Given a document not in the model, returns a vector in LSA concept space.
This happes automatically in Model.cosine_similarity(),
but it must be done explicitly for Classifier.classify() input.
"""
if document.id in self.u:
return self.u[document.id]
if document.id in _lsa_transform_cache:
return _lsa_transform_cache[document.id]
import numpy
v = self.model.vector(document)
v = [v[self._terms[i]] for i in range(len(v))]
v = np.dot(np.dot(np.linalg.inv(np.diag(self.sigma)), self.vt), v)
v = _lsa_transform_cache[document.id] = Vector(enumerate(v))
return v
# LSA cache for Model.vector_space_search() shouldn't be stored with Model.save()
# (so it is a global instead of a property of the LSA class).
_lsa_transform_cache = {}
#def iter2array(iterator, typecode):
# a = np.array([next(iterator)], typecode)
# shape0 = a.shape[1:]
# for (i, item) in enumerate(iterator):
# a.resize((i+2,) + shape0)
# a[i+1] = item
# return a
#def filter(matrix, min=0):
# columns = np.max(matrix, axis=0)
# columns = [i for i, v in enumerate(columns) if v <= min] # Indices of removed columns.
# matrix = np.delete(matrix, columns, axis=1)
# return matrix, columns
#### CLUSTERING ####################################################################################
# Clustering can be used to categorize a set of unlabeled documents.
# Clustering is an unsupervised machine learning method that partitions a set of vectors into
# subsets, using a distance metric to determine how similar two vectors are.
# For example, for (x, y)-points in 2D space we can use Euclidean distance ("as the crow flies").
# The k_means() and hierarchical() functions work with Vector objects or dictionaries.
def mean(iterable, length=None):
""" Returns the arithmetic mean of the values in the given iterable or iterator.
"""
if length is None:
if not hasattr(iterable, "__len__"):
iterable = list(iterable)
length = len(iterable)
return sum(iterable) / float(length or 1)
def centroid(vectors=[], features=[]):
""" Returns the center of the given list of vectors.
For example: if each vector has two features, (x, y)-coordinates in 2D space,
the centroid is the geometric center of the coordinates forming a polygon.
Since vectors are sparse (i.e., features with weight 0 are omitted),
the list of all features (= Model.vector) must be given.
"""
c = []
for v in vectors:
if isinstance(v, Cluster):
c.extend(v.flatten())
elif isinstance(v, Document):
c.append(v.vector)
else:
c.append(v)
if not features:
features = _features(c)
c = [(f, mean((v.get(f, 0) for v in c), len(c))) for f in features]
c = Vector((f, w) for f, w in c if w != 0)
return c
class DistanceMap(object):
def __init__(self, method=COSINE):
""" A lazy map of cached distances between Vector objects.
"""
self.method = method
self._cache = {}
def __call__(self, v1, v2):
return self.distance(v1, v2)
def distance(self, v1, v2):
""" Returns the cached distance between two vectors.
"""
try:
# Two Vector objects for which the distance was already calculated.
d = self._cache[(v1.id, v2.id)]
except KeyError:
# Two Vector objects for which the distance has not been calculated.
d = self._cache[(v1.id, v2.id)] = distance(v1, v2, method=self.method)
except AttributeError:
# No "id" property, so not a Vector but a plain dict.
d = distance(v1, v2, method=self.method)
return d
def cluster(method=KMEANS, vectors=[], **kwargs):
""" Clusters the given list of vectors using the k-means or hierarchical algorithm.
"""
if method == KMEANS:
return k_means(vectors, **kwargs)
if method == HIERARCHICAL:
return hierarchical(vectors, **kwargs)
#--- K-MEANS ---------------------------------------------------------------------------------------
# k-means is fast but no optimal solution is guaranteed (random initialization).
# Initialization methods:
RANDOM, KMPP = "random", "kmeans++"
def k_means(vectors, k=None, iterations=10, distance=COSINE, seed=RANDOM, **kwargs):
""" Returns a list of k clusters, where each cluster is a list of vectors (Lloyd's algorithm).
Vectors are assigned to k random centers using a distance metric (EUCLIDEAN, COSINE, ...).
Since the initial centers are chosen randomly (by default, seed=RANDOM),
there is no guarantee of convergence or of finding an optimal solution.
A more efficient way is to use seed=KMPP (k-means++ initialization algorithm).
"""
features = kwargs.get("features") or _features(vectors)
if k is None:
k = sqrt(len(vectors) / 2)
if k < 2:
return [[v for v in vectors]]
if seed == KMPP:
clusters = kmpp(vectors, k, distance)
else:
clusters = [[] for i in range(int(k))]
for i, v in enumerate(sorted(vectors, key=lambda x: random())):
# Randomly partition the vectors across k clusters.
clusters[i % int(k)].append(v)
# Cache the distance calculations between vectors (up to 4x faster).
map = DistanceMap(method=distance)
distance = map.distance
converged = False
while not converged and iterations > 0 and k > 0:
# Calculate the center of each cluster.
centroids = [centroid(cluster, features) for cluster in clusters]
# Triangle inequality: one side is shorter than the sum of the two other sides.
# We can exploit this to avoid costly distance() calls (up to 3x faster).
p = 0.5 * kwargs.get("p", 0.8) # "Relaxed" triangle inequality (cosine distance is a semimetric) 0.25-0.5.
D = {}
for i in range(len(centroids)):
for j in range(i, len(centroids)): # center1–center2 < center1–vector + vector–center2 ?
D[(i, j)] = D[(j, i)] = p * distance(centroids[i], centroids[j])
# For every vector in every cluster,
# check if it is nearer to the center of another cluster.
# If so, assign it. When visualized, this produces a Voronoi diagram.
converged = True
for i in range(len(clusters)):
for v in clusters[i]:
nearest, d1 = i, distance(v, centroids[i])
for j in range(len(clusters)):
if D[(i, j)] < d1: # Triangle inequality (Elkan, 2003).
d2 = distance(v, centroids[j])
if d2 < d1:
nearest = j
if nearest != i: # Other cluster is nearer.
clusters[nearest].append(clusters[i].pop(clusters[i].index(v)))
converged = False
iterations -= 1
#print(iterations)
return clusters
kmeans = k_means
def kmpp(vectors, k, distance=COSINE):
""" The k-means++ initialization algorithm returns a set of initial clusers,
with the advantage that:
- it generates better clusters than k-means(seed=RANDOM) on most data sets,
- it runs faster than standard k-means,
- it has a theoretical approximation guarantee.
"""
# Cache the distance calculations between vectors (up to 4x faster).
map = DistanceMap(method=distance)
distance = map.distance
# David Arthur, 2006, http://theory.stanford.edu/~sergei/slides/BATS-Means.pdf
# Based on:
# http://www.stanford.edu/~darthur/kmpp.zip
# http://yongsun.me/2008/10/k-means-and-k-means-with-python
# Choose one center at random.
# Calculate the distance between each vector and the nearest center.
centroids = [choice(vectors)]
d = [distance(v, centroids[0]) for v in vectors]
s = sum(d)
for _ in range(int(k) - 1):
# Choose a random number y between 0 and d1 + d2 + ... + dn.
# Find vector i so that: d1 + d2 + ... + di >= y > d1 + d2 + ... + dj.
# Perform a number of local tries so that y yields a small distance sum.
i = 0
for _ in range(int(2 + log(k))):
y = random() * s
for i1, v1 in enumerate(vectors):
if y <= d[i1]:
break
y -= d[i1]
s1 = sum(min(d[j], distance(v1, v2)) for j, v2 in enumerate(vectors))
if s1 < s:
s, i = s1, i1
# Add vector i as a new center.
# Repeat until we have chosen k centers.
centroids.append(vectors[i])
d = [min(d[i], distance(v, centroids[-1])) for i, v in enumerate(vectors)]
s = sum(d)
# Assign points to the nearest center.
clusters = [[] for i in range(int(k))]
for v1 in vectors:
d = [distance(v1, v2) for v2 in centroids]
clusters[d.index(min(d))].append(v1)
return clusters
#--- HIERARCHICAL ----------------------------------------------------------------------------------
# Hierarchical clustering is slow but the optimal solution guaranteed in O(len(vectors) ** 3).
class Cluster(list):
def __init__(self, *args, **kwargs):
""" A nested list of Cluster and Vector objects,
returned from hierarchical() clustering.
"""
list.__init__(self, *args, **kwargs)
@property
def depth(self):
""" Yields the maximum depth of nested clusters.
Cluster((1, Cluster((2, Cluster((3, 4)))))).depth => 2.
"""
return max([0] + [1 + n.depth for n in self if isinstance(n, Cluster)])
def flatten(self, depth=1000):
""" Flattens nested clusters to a list, down to the given depth.
Cluster((1, Cluster((2, Cluster((3, 4)))))).flatten(1) => [1, 2, Cluster(3, 4)].
"""
a = []
for item in self:
if isinstance(item, Cluster) and depth > 0:
a.extend(item.flatten(depth - 1))
else:
a.append(item)
return a
def traverse(self, visit=lambda cluster: None):
""" Calls the given visit() function on this cluster and each nested cluster, breadth-first.
"""
visit(self)
for item in self:
if isinstance(item, Cluster):
item.traverse(visit)
def __repr__(self):
return "Cluster(%s)" % list.__repr__(self)
def sequence(i=0, f=lambda i: i + 1):
""" Yields an infinite sequence, for example:
sequence() => 0, 1, 2, 3, ...
sequence(1.0, lambda i: i/2) => 1, 0.5, 0.25, 0.125, ...
"""
# Used to generate unique vector id's in hierarchical().
# We cannot use Vector.id, since the given vectors might be plain dicts.
# We cannot use id(vector), since id() is only unique for the lifespan of the object.
while True:
yield i
i = f(i)
def hierarchical(vectors, k=1, iterations=1000, distance=COSINE, **kwargs):
""" Returns a Cluster containing k items (vectors or clusters with nested items).
With k=1, the top-level cluster contains a single cluster.
"""
id = sequence()
features = kwargs.get("features", _features(vectors))
clusters = Cluster((v for v in shuffled(vectors)))
centroids = [(next(id), v) for v in clusters]
map = {}
for _ in range(iterations):
if len(clusters) <= max(k, 1):
break
nearest, d0 = None, None
for i, (id1, v1) in enumerate(centroids):
for j, (id2, v2) in enumerate(centroids[i + 1:]):
# Cache the distance calculations between vectors.
# This is identical to DistanceMap.distance(),
# but it is faster in the inner loop to use it directly.
try:
d = map[(id1, id2)]
except KeyError:
d = map[(id1, id2)] = _distance(v1, v2, method=distance)
if d0 is None or d < d0:
nearest, d0 = (i, j + i + 1), d
# Pairs of nearest clusters are merged as we move up the hierarchy:
i, j = nearest
merged = Cluster((clusters[i], clusters[j]))
clusters.pop(j)
clusters.pop(i)
clusters.append(merged)
# Cache the center of the new cluster.
v = centroid(merged.flatten(), features)
centroids.pop(j)
centroids.pop(i)
centroids.append((next(id), v))
return clusters
#from pattern.vector import Vector
#
#v1 = Vector(wings=0, beak=0, claws=1, paws=1, fur=1) # cat
#v2 = Vector(wings=0, beak=0, claws=0, paws=1, fur=1) # dog
#v3 = Vector(wings=1, beak=1, claws=1, paws=0, fur=0) # bird
#
#print(hierarchical([v1, v2, v3]))
#### CLASSIFIER ####################################################################################
# Classification can be used to predict the label of an unlabeled document.
# Classification is a supervised machine learning method that uses labeled documents
# (i.e., Document objects with a type) as training examples to statistically predict
# the label (type, class) of new documents, based on their similarity to the training examples
# using a distance metric (e.g., cosine similarity).
#--- CLASSIFIER BASE CLASS -------------------------------------------------------------------------
# The default baseline (i.e., the default predicted class) is the most frequent class:
MAJORITY, FREQUENCY = "majority", "frequency"
class Classifier(object):
def __init__(self, train=[], baseline=MAJORITY, **kwargs):
""" A base class for Naive Bayes, k-NN and SVM.
Trains a classifier on the given list of Documents or (document, type)-tuples,
where document can be a Document, Vector, dict or string
(dicts and strings are implicitly converted to vectors).
"""
data = getattr(self, "_data", {})
self.description = "" # Description of the dataset: author e-mail, etc.
self._data = data # Custom data to store when pickled.
self._vectors = [] # List of trained (type, vector)-tuples.
self._classes = {} # Dict of (class, frequency)-items.
self._baseline = baseline # Default predicted class.
# Train on the list of Document objects or (document, type)-tuples:
for d in (isinstance(d, Document) and (d, d.type) or d for d in train):
self.train(*d)
# In Pattern 2.5-, Classifier.test() is a classmethod.
# In Pattern 2.6+, it is replaced with Classifier._test() once instantiated:
self.test = self._test
@property
def features(self):
""" Yields a list of trained features.
"""
return list(features(v for type, v in self._vectors))
@property
def classes(self):
""" Yields a list of trained classes.
"""
return list(self._classes.keys())
terms, types = features, classes
@property
def binary(self):
""" Yields True if the classifier predicts either True (0) or False (1).
"""
return sorted(self.classes) in ([False, True], [0, 1])
@property
def distribution(self):
""" Yields a dictionary of trained (class, frequency)-items.
"""
return self._classes.copy()
@property
def majority(self):
""" Yields the majority class (= most frequent class).
"""
d = sorted((v, k) for k, v in self._classes.items())
return d and d[-1][1] or None
@property
def minority(self):
""" Yields the minority class (= least frequent class).
"""
d = sorted((v, k) for k, v in self._classes.items())
return d and d[0][1] or None
@property
def baseline(self):
""" Yields the most frequent class in the training data,
or a user-defined class if Classifier(baseline != MAJORITY).
"""
if self._baseline not in (MAJORITY, FREQUENCY):
return self._baseline
return ([(0, None)] + sorted([(v, k) for k, v in self._classes.items()]))[-1][1]
@property
def weighted_random_baseline(self):
""" Yields the weighted random baseline:
accuracy with classes predicted randomly according to their distribution.
"""
n = float(sum(self.distribution.values())) or 1
return sum(map(lambda x: (x / n) ** 2, self.distribution.values()))
wrb = weighted_random_baseline
@property
def skewness(self):
""" Yields 0.0 if the trained classes are evenly distributed.
Yields > +1.0 or < -1.0 if the training data is highly skewed.
"""
def moment(a, m, k=1):
return sum([(x - m)**k for x in a]) / (len(a) or 1)
# List each training instance by an int that represents its class:
a = list(chain(*([i] * v for i, (k, v) in enumerate(self._classes.items()))))
m = float(sum(a)) / len(a) # mean
return moment(a, m, 3) / (moment(a, m, 2) ** 1.5 or 1)
def train(self, document, type=None):
""" Trains the classifier with the given document of the given type (i.e., class).
A document can be a Document, Vector, dict, list or string.
If no type is given, Document.type will be used instead.
"""
type, vector = self._vector(document, type)
self._vectors.append((type, vector))
self._classes[type] = self._classes.get(type, 0) + 1
def classify(self, document, discrete=True):
""" Returns the type with the highest probability for the given document.
Returns a dict of (class, probability)-items if discrete=False.
"""
# This method must be implemented in subclass.
if not discrete:
return Probabilities(self, {})
return self.baseline
def _vector(self, document, type=None, **kwargs):
""" Returns a (type, Vector)-tuple for the given document.
If the document is part of a LSA-reduced model, returns the LSA concept vector.
If the given type is None, returns document.type (if a Document is given).
"""
if isinstance(document, Document):
if type is None:
type = document.type
if document.model and document.model.lsa:
return type, document.model.lsa[document.id] # LSA concept vector.
return type, document.vector
if isinstance(document, Vector):
return type, document
if isinstance(document, dict):
return type, Vector(document, **kwargs)
if isinstance(document, (list, tuple)):
return type, Document(document, filter=None, stopwords=True).vector
if isinstance(document, str):
return type, Document(document, filter=None, stopwords=True).vector
@classmethod
def k_fold_cross_validation(cls, corpus=[], k=10, **kwargs):
# Backwards compatibility.
return K_fold_cross_validation(cls, documents=corpus, folds=k, **kwargs)
crossvalidate = cross_validate = cv = k_fold_cross_validation
@classmethod
def test(cls, corpus=[], d=0.65, folds=1, **kwargs):
# Backwards compatibility.
# In Pattern 2.5-, Classifier.test() is a classmethod.
# In Pattern 2.6+, it is replaced with Classifier._test() once instantiated.
corpus = kwargs.pop("documents", kwargs.pop("train", corpus))
if folds > 1:
return K_fold_cross_validation(cls, documents=corpus, folds=folds, **kwargs)
i = int(round(max(0.0, min(1.0, d)) * len(corpus)))
d = shuffled(corpus)
return cls(train=d[:i], **kwargs).test(d[i:])
def _test(self, documents=[], target=None, **kwargs):
""" Returns an (accuracy, precision, recall, F1-score)-tuple for the given documents,
with values between 0.0 and 1.0 (0-100%).
Accuracy is the percentage of correct predictions for the given test set,
but this metric can be misleading (e.g., classifier *always* predicts True).
Precision is the percentage of predictions that were correct.
Recall is the percentage of documents that were correctly labeled.
F1-score is the harmonic mean of precision and recall.
"""
return self.confusion_matrix(documents).test(target)
def auc(self, documents=[], k=10):
""" Returns the area under the ROC-curve.
Returns the probability (0.0-1.0) that a classifier will rank
a random positive document (True) higher than a random negative one (False).
"""
return self.confusion_matrix(documents).auc(k)
def confusion_matrix(self, documents=[]):
""" Returns the confusion matrix for the given test data,
which is a list of Documents or (document, type)-tuples.
"""
documents = [isinstance(d, Document) and (d, d.type) or d for d in documents]
return ConfusionMatrix(self.classify, documents)
def save(self, path, final=False):
""" Saves the classifier as a gzipped pickle file.
"""
if final:
self.finalize()
self.test = None # Can't pickle instancemethods.
f = gzip.GzipFile(path, "wb")
f.write(pickle.dumps(self, 1)) # 1 = binary
f.close()
@classmethod
def load(cls, path):
""" Loads the classifier from a gzipped pickle file.
"""
f = gzip.GzipFile(path, "r")
self = pickle.loads(f.read())
self._on_load(path) # Initialize subclass (e.g., SVM).
self.test = self._test
f.close()
return self
def _on_load(self, path):
pass
def finalize(self):
""" Removes training data from memory, keeping only the trained model,
reducing file size with Classifier.save().
"""
pass
#--- CLASSIFIER PROBABILITIES ----------------------------------------------------------------------
# Returned from Classifier.classify(v, discrete=False)
class Probabilities(defaultdict):
def __init__(self, classifier, *args, **kwargs):
defaultdict.__init__(self, float)
self.classifier = classifier
self.update(*args, **kwargs)
@property
def max(self):
""" Returns the (type, probability) with the highest probability.
"""
try:
# Ties are broken in favor of the majority class
# (random winner for majority ties).
b = self.classifier.baseline
m = list(self.values())
m = max(m)
if self[b] < m:
return choice([x for x in self if self[x] == m > 0]), m
return b, m
except:
return b, 0.0
#--- CLASSIFIER EVALUATION -------------------------------------------------------------------------
class ConfusionMatrix(defaultdict):
def __init__(self, classify=lambda document: True, documents=[]):
""" Returns the matrix of classes x predicted classes as a dictionary.
"""
defaultdict.__init__(self, lambda: defaultdict(int))
for document, type1 in documents:
type2 = classify(document)
self[type1][type2] += 1
def split(self):
""" Returns an iterator over one-vs-all (type, TP, TN, FP, FN)-tuples.
"""
return iter((type,) + self(type) for type in self)
def __call__(self, target):
""" Returns a (TP, TN, FP, FN)-tuple for the given class (one-vs-all).
"""
TP = 0 # True positives.
TN = 0 # True negatives.
FP = 0 # False positives (type I error).
FN = 0 # False negatives (type II error).
for t1 in self:
for t2, n in self[t1].items():
if target == t1 == t2:
TP += n
if target != t1 == t2:
TN += n
if target == t1 != t2:
FN += n
if target == t2 != t1:
FP += n
return (TP, TN, FP, FN)
def test(self, target=None):
""" Returns an (accuracy, precision, recall, F1-score)-tuple.
"""
A = [] # Accuracy.
P = [] # Precision.
R = [] # Recall.
for type, TP, TN, FP, FN in self.split():
if type == target or target is None:
# Calculate precision & recall per class.
A.append(float(TP + TN) / ((TP + TN + FP + FN)))
P.append(float(TP) / ((TP + FP) or 1))
R.append(float(TP) / ((TP + FN) or 1))
# Macro-averaged:
A = sum(A) / (len(A) or 1.0)
P = sum(P) / (len(P) or 1.0)
R = sum(R) / (len(R) or 1.0)
F = 2.0 * P * R / ((P + R) or 1.0)
return A, P, R, F
def auc(self, k=10):
""" Returns the area under the ROC-curve.
"""
roc = [(0.0, 0.0), (1.0, 1.0)]
for type, TP, TN, FP, FN in self.split():
x = FPR = float(FP) / ((FP + TN) or 1) # false positive rate
y = TPR = float(TP) / ((TP + FN) or 1) # true positive rate
roc.append((x, y))
#print("%s\t%s %s %s %s\t %s %s" % (TP, TN, FP, FN, FPR, TPR))
roc = sorted(roc)
# Trapzoidal rule: area = (a + b) * h / 2, where a=y0, b=y1 and h=x1-x0.
return sum(0.5 * (x1 - x0) * (y1 + y0) for (x0, y0), (x1, y1) in sorted(zip(roc, roc[1:])))
@property
def table(self, padding=1):
""" Returns the matrix as a string with rows and columns.
"""
k = sorted(self)
n = max([len(decode_utf8(x)) for x in k])
n = max(n, *(len(str(self[k1][k2])) for k1 in k for k2 in k)) + padding
s = "".ljust(n)
for t1 in k:
s += decode_utf8(t1).ljust(n)
for t1 in k:
s += "\n"
s += decode_utf8(t1).ljust(n)
for t2 in k:
s += str(self[t1][t2]).ljust(n)
return s
def __repr__(self):
return repr(dict((k, dict(v)) for k, v in self.items()))
def K_fold_cross_validation(Classifier, documents=[], folds=10, **kwargs):
""" Returns an (accuracy, precisiom, recall, F1-score, standard deviation)-tuple.
For 10-fold cross-validation, performs 10 separate tests of the classifier,
each with a different 9/10 training and 1/10 testing documents.
The given list of documents contains Documents or (document, type)-tuples.
The given classifier is a class (NB, KNN, SLP, SVM)
which is initialized with the given optional parameters.
"""
K = kwargs.pop("K", folds)
s = kwargs.pop("shuffled", True)
# Macro-average accuracy, precision, recall & F1-score.
m = [0.0, 0.0, 0.0, 0.0]
f = []
# Create shuffled folds to avoid a list sorted by type
# (we take successive folds and the source data could be sorted).
if isinstance(K, (int, float)):
folds = list(_folds(shuffled(documents) if s else documents, K))
# K tests with different train (d1) and test (d2) sets.
for d1, d2 in folds:
d1 = [isinstance(d, Document) and (d, d.type) or d for d in d1]
d2 = [isinstance(d, Document) and (d, d.type) or d for d in d2]
classifier = Classifier(train=d1, **kwargs)
A, P, R, F = classifier.test(d2, **kwargs)
m[0] += A
m[1] += P
m[2] += R
m[3] += F
f.append(F)
# F-score mean & variance.
K = len(folds)
u = float(sum(f)) / (K or 1.0)
o = float(sum((x - u) ** 2 for x in f)) / (K - 1 or 1.0)
o = sqrt(o)
return tuple([v / (K or 1.0) for v in m] + [o])
kfoldcv = K_fold_cv = k_fold_cv = k_fold_cross_validation = K_fold_cross_validation
def folds(documents=[], K=10, **kwargs):
""" Returns an iterator of K folds, where each fold is a (train, test)-tuple.
For example, for 10-fold cross-validation, it yields 10 tuples,
each with a different 9/10 training and 1/10 testing documents.
"""
def chunks(iterable, n=10):
# Returns an iterator of n lists of roughly equal size.
# http://www.garyrobinson.net/2008/04/splitting-a-pyt.html
a = list(iterable)
i = 0
j = 0
for m in range(n):
j = i + len(a[m::n])
yield a[i:j]
i = j
k = kwargs.get("k", K)
d = list(chunks(documents, max(k, 2)))
for holdout in range(k):
yield list(chain(*(d[:holdout] + d[holdout + 1:]))), d[holdout]
_folds = folds
def gridsearch(Classifier, documents=[], folds=10, **kwargs):
""" Returns the test results for every combination of optional parameters,
using K-fold cross-validation for the given classifier (NB, KNN, SLP, SVM).
For example:
for (A, P, R, F, o), p in gridsearch(SVM, data, c=[0.1, 1, 10]):
print((A, P, R, F, o), p)
> (0.919, 0.921, 0.919, 0.920), {"c": 10}
> (0.874, 0.884, 0.865, 0.874), {"c": 1}
> (0.535, 0.424, 0.551, 0.454), {"c": 0.1}
"""
def product(*args):
# Yields the cartesian product of given iterables:
# list(product([1, 2], [3, 4])) => [(1, 3), (1, 4), (2, 3), (2, 4)]
p = [[]]
for iterable in args:
p = [x + [y] for x in p for y in iterable]
for p in p:
yield tuple(p)
s = [] # [((A, P, R, F, o), parameters), ...]
p = [] # [[("c", 0.1), ("c", 10), ...],
# [("gamma", 0.1), ("gamma", 0.2), ...], ...]
for k, v in kwargs.items():
p.append([(k, v) for v in v])
for p in product(*p):
p = dict(p)
s.append((K_fold_cross_validation(Classifier, documents, folds, **p), p))
return sorted(s, reverse=True)
def feature_selection(documents=[], top=None, method=CHISQUARED, threshold=0.0):
""" Returns an iterator of (feature, weight, (probability, class))-tuples,
sorted by the given feature selection method (IG, GR, X2) and document frequency threshold.
"""
a = []
for d in documents:
if not isinstance(d, Document):
d = Document(d[0], type=d[1], stopwords=True)
a.append(d)
m = Model(a, weight=None)
p = m.posterior_probability
c = m.classes
for w, f in m.feature_selection(top, method, threshold, weighted=True):
# For each feature, retrieve the class with the maximum probabilty.
yield f, w, max([(p(f, type), type) for type in c])
fsel = feature_selection
#--- NAIVE BAYES CLASSIFIER ------------------------------------------------------------------------
MULTINOMIAL = "multinomial" # Feature weighting.
BINOMIAL = "binomial" # Feature occurs in class (1) or not (0).
BERNOUILLI = "bernouilli" # Feature occurs in class (1) or not (0).
class NB(Classifier):
def __init__(self, train=[], baseline=MAJORITY, method=MULTINOMIAL, alpha=0.0001, **kwargs):
""" Naive Bayes is a simple supervised learning method for text classification.
Documents are classified based on the probability that a feature occurs in a class
(independent of other features).
"""
self._classes = {} # {class: frequency}
self._features = {} # {feature: frequency}
self._likelihood = {} # {class: {feature: frequency}}
self._cache = {} # Cache log likelihood sums.
self._method = method # MULTINOMIAL or BERNOUILLI.
self._alpha = alpha # Smoothing.
Classifier.__init__(self, train, baseline)
@property
def method(self):
return self._method
@property
def features(self):
return list(self._features.keys())
def train(self, document, type=None):
""" Trains the classifier with the given document of the given type (i.e., class).
A document can be a Document, Vector, dict, list or string.
If no type is given, Document.type will be used instead.
"""
# Calculate the probability of a class.
# Calculate the probability of a feature.
# Calculate the probability of a feature occuring in a class (= conditional probability).
type, vector = self._vector(document, type=type)
self._classes[type] = self._classes.get(type, 0) + 1
self._likelihood.setdefault(type, {})
self._cache.pop(type, None)
for f, w in vector.items():
if self._method in (BINARY, BINOMIAL, BERNOUILLI):
w = 1
self._features[f] = self._features.get(f, 0) + 1
self._likelihood[type][f] = self._likelihood[type].get(f, 0) + w
def classify(self, document, discrete=True):
""" Returns the type with the highest probability for the given document.
If the classifier has been trained on LSA concept vectors
you need to supply LSA.transform(document).
"""
# Given red & round, what is the likelihood that it is an apple?
# p = p(red|apple) * p(round|apple) * p(apple) / (p(red) * p(round))
# The multiplication can cause underflow so we use log() instead.
# For unknown features, we smoothen with an alpha value.
v = self._vector(document)[1]
m = self._method
a = self._alpha
n = self._classes.values()
n = float(sum(n))
p = defaultdict(float)
for type in self._classes:
if m == MULTINOMIAL:
if type not in self._cache: # 10x faster
self._cache[type] = float(sum(self._likelihood[type].values()))
d = self._cache[type]
if m == BINOMIAL \
or m == BERNOUILLI:
d = float(self._classes[type])
L = self._likelihood[type]
g = sum(log((L[f] if f in L else a) / d) for f in v)
g = exp(g) * self._classes[type] / n # prior
p[type] = g
# Normalize probability estimates.
s = sum(p.values()) or 1
for type in p:
p[type] /= s
if not discrete:
return Probabilities(self, p)
try:
# Ties are broken in favor of the majority class
# (random winner for majority ties).
m = max(p.values())
p = sorted((self._classes[type], type) for type, g in p.items() if g == m > 0)
p = [type for frequency, type in p if frequency == p[0][0]]
return choice(p)
except:
return self.baseline
Bayes = NaiveBayes = NB
#--- K-NEAREST NEIGHBOR CLASSIFIER -----------------------------------------------------------------
class KNN(Classifier):
def __init__(self, train=[], baseline=MAJORITY, k=10, distance=COSINE, **kwargs):
""" k-nearest neighbor (kNN) is a simple supervised learning method for text classification.
Documents are classified by a majority vote of nearest neighbors (cosine distance)
in the training data.
"""
self.k = k # Number of nearest neighbors to observe.
self.distance = distance # COSINE, EUCLIDEAN, ...
Classifier.__init__(self, train, baseline)
def train(self, document, type=None):
""" Trains the classifier with the given document of the given type (i.e., class).
A document can be a Document, Vector, dict, list or string.
If no type is given, Document.type will be used instead.
"""
Classifier.train(self, document, type)
def classify(self, document, discrete=True):
""" Returns the type with the highest probability for the given document.
If the classifier has been trained on LSA concept vectors
you need to supply LSA.transform(document).
"""
# Distance is calculated between the document vector and all training instances.
# This will make KNN slow in higher dimensions.
classes = {}
v1 = self._vector(document)[1]
D = ((distance(v1, v2, method=self.distance), type) for type, v2 in self._vectors)
D = ((d, type) for d, type in D if d < 1) # Nothing in common if distance=1.0.
D = heapq.nsmallest(self.k, D) # k-least distant.
# Normalize probability estimates.
s = sum(1 - d for d, type in D) or 1
p = defaultdict(float)
for d, type in D:
p[type] += (1 - d) / s
if not discrete:
return Probabilities(self, p)
try:
# Ties are broken in favor of the majority class
# (random winner for majority ties).
m = max(p.values())
p = sorted((self._classes[type], type) for type, w in p.items() if w == m > 0)
p = [type for frequency, type in p if frequency == p[0][0]]
return choice(p)
except:
return self.baseline
NearestNeighbor = kNN = KNN
#from pattern.vector import Document, KNN
#
#d1 = Document("cats have stripes, purr and drink milk", type="cat")
#d2 = Document("cows are black and white, they moo and give milk", type="cow")
#d3 = Document("birds have wings and can fly", type="bird")
#
#knn = KNN()
#for d in (d1, d2, d3):
# knn.train(d)
#
#print(knn.binary)
#print(knn.classes)
#print(knn.classify(Document("something that can fly")))
#print(KNN.test((d1, d2, d3), folds=2))
#--- INFORMATION GAIN TREE --------------------------------------------------------------------------
class IGTreeNode(list):
def __init__(self, feature=None, value=None, type=None):
self.feature = feature
self.value = value
self.type = type
@property
def children(self):
return self
@property
def leaf(self):
return len(self) == 0
class IGTree(Classifier):
def __init__(self, train=[], baseline=MAJORITY, method=GAINRATIO, **kwargs):
""" IGTREE is a supervised learning method
where training data is represented as a tree ordered by information gain.
A feature is taken to occur in a vector (1) or not (0), i.e. BINARY weight.
"""
self._root = None
self._method = method
Classifier.__init__(self, train, baseline)
@property
def method(self):
return self._method
def _tree(self, vectors=[], features=[]):
""" Returns a tree of nested IGTREE.Node objects,
where the given list of vectors contains (Vector, class)-tuples, and
where the given list of features is sorted by information gain ratio.
"""
# Daelemans, W., van den Bosch, A., Weijters, T. (1997).
# IGTree: Using trees for compression and classification in lazy learning algorithms.
# Artificial Intelligence Review 11, 407-423.
vectors = list(vectors)
features = list(features)
if len(vectors) == 0 or len(features) == 0:
return IGTreeNode()
# {class: count}
classes = defaultdict(int)
for v, type in vectors:
classes[type] += 1
# Find the most frequent class for the set of vectors.
c = max(classes, key=classes.__getitem__)
# Find the most informative feature f.
f = features[0]
n = IGTreeNode(feature=f, type=c)
# The current node has a hyperplane on feature f,
# and the majority class in the set of vectors.
if len(classes) == 1:
return n
if len(features) == 1:
return n
# Partition the set of vectors into subsets
# (vectors with the same value for feature f are in the same subset).
p = defaultdict(list)
for v, type in vectors:
#x = round(v.get(f, 0.0), 1)
x = f in v
p[x].append((v, type))
# If not all vectors in a subset have the same class,
# build IGTREE._tree(subset, features[1:]) and connect it to the current node.
for x in p:
if any((type != c) for v, type in p[x]):
n.append(self._tree(p[x], features[1:]))
n[-1].value = x
# print(n.type)
# print(n[-1].type)
return n
def _search(self, node, vector):
""" Returns the predicted class for the given Vector.
"""
while True:
#x = round(vector.get(node.feature, 0.0), 1)
x = node.feature in vector
b = False
for n in node.children:
if n.value == x:
b = True
break
if b is False:
return node.type
node = n
def train(self, document, type=None):
Classifier.train(self, document, type)
def _train(self):
""" Calculates information gain ratio for the features in the training data.
Constructs the search tree.
"""
m = Model((Document(set(v), type=type) for type, v in self._vectors), weight=BINARY)
f = sorted(m.features, key=getattr(m, self._method), reverse=True)
# print(f)
# print(self._vectors)
sys.setrecursionlimit(max(len(f) * 2, 1000))
self._root = self._tree([(v, type) for type, v in self._vectors], features=f)
def classify(self, document, discrete=True):
""" Returns the type with the highest probability for the given document.
If the classifier has been trained on LSA concept vectors
you need to supply LSA.transform(document).
"""
if self._root is None:
self._train()
res = self._search(self._root, self._vector(document)[1])
return res
def finalize(self):
""" Removes training data from memory, keeping only the IG tree,
reducing file size with Classifier.save().
"""
if self._root is None:
self._train()
self._vectors = []
IGTREE = IGTree
doc1 = (''' "Dynamic Programming is a very powerful mathematical technique, often utilised in programming, for solving optimization problems. Normally, minimizing or maximizing.
‘Greedy’ algorithms focus on making the best local choice at each decision making stage. Without a proof of correctness, such an algorithm is likely to fail. With Dynamic Programming, we can design our own algorithm which searches for all possibilities (which ensures correctness) whilst storing the results to avoid having to recomputed (leading to computational efficiency).
Dynamic Programming solves problems by combining the solutions of subproblems. These subproblems are not, however, independent. Subproblems can share subsubproblems, but the solution to one subproblem doesn’t necessarily affect the solutions to other subproblems stemming from the same problem.
Dynamic Programming reduces computation time by solving subproblems in a ‘bottom-up’ way. It stores the solution to a subproblem the first time it is solved, meaning that it can look up the solution when that subproblem is encountered subsequently.
The key to Dynamic Programming is to find the structure of optimal solutions. The steps required are as follows:
1. Generalise the structure of an optimal solution
2. Recursively define the value of an optimal solution
3. Compute the optimal solution values either top-down (with caching), or bottom-up using a table
4. Generate the optimal solution of these computed values
","In mathematics and computer science, dynamic programming is a method of solving problems that exhibit the properties of overlapping subproblems and optimal substructure (described below). The method takes much less time than naive methods.
The term was originally used in the 1940s by Richard Bellman to describe the process of solving problems where one needs to find the best decisions one after another. By 1953, he had refined this to the modern meaning. The field was founded as a systems analysis and engineering topic that is recognized by the IEEE. Bellman's contribution is remembered in the name of the Bellman equation, a central result of dynamic programming which restates an optimization problem in recursive form.
The word ""programming"" in ""dynamic programming"" has no particular connection to computer programming at all, and instead comes from the term ""mathematical programming"", a synonym for optimization. Thus, the ""program"" is the optimal plan for action that is produced. For instance, a finalized schedule of events at an exhibition is sometimes called a program. Programming, in this sense, means finding an acceptable plan of action, an algorithm.
Optimal substructure means that optimal solutions of subproblems can be used to find the optimal solutions of the overall problem. For example, the shortest path to a goal from a vertex in a graph can be found by first computing the shortest path to the goal from all adjacent vertices, and then using this to pick the best overall path, as shown in Figure 1. In general, we can solve a problem with optimal substructure using a three-step process:
1. Break the problem into smaller subproblems.
2. Solve these problems optimally using this three-step process recursively.
3. Use these optimal solutions to construct an optimal solution for the original problem.
The subproblems are, themselves, solved by dividing them into sub-subproblems, and so on, until we reach some simple case that is solvable in constant time.
Figure 2. The subproblem graph for the Fibonacci sequence. That it is not a tree but a DAG indicates overlapping subproblems.
To say that a problem has overlapping subproblems is to say that the same subproblems are used to solve many different larger problems. For example, in the Fibonacci sequence, F3 = F1 + F2 and F4 = F2 + F3 — computing each number involves computing F2. Because both F3 and F4 are needed to compute F5, a naive approach to computing F5 may end up computing F2 twice or more. This applies whenever overlapping subproblems are present: a naive approach may waste time recomputing optimal solutions to subproblems it has already solved.
In order to avoid this, we instead save the solutions to problems we have already solved. Then, if we need to solve the same problem later, we can retrieve and reuse our already-computed solution. This approach is called memoization (not memorization, although this term also fits). If we are sure we won't need a particular solution anymore, we can throw it away to save space. In some cases, we can even compute the solutions to subproblems we know that we'll need in advance.
''', True)
doc2 = ('''" In probability theory, Bayes' theorem (often called Bayes' law after Rev Thomas Bayes) relates the conditional and marginal probabilities of two random events. It is often used to compute posterior probabilities given observations. For example, a patient may be observed to have certain symptoms. Bayes' theorem can be used to compute the probability that a proposed diagnosis is correct, given that observation.
As a formal theorem, Bayes' theorem is valid in all common interpretations of probability. However, it plays a central role in the debate around the foundations of statistics: frequentist and Bayesian interpretations disagree about the ways in which probabilities should be assigned in applications.
Suppose there is a co-ed school having 60% boys and 40% girls as students. The girl students wear trousers or skirts in equal numbers; the boys all wear trousers. An observer sees a (random) student from a distance; all they can see is that this student is wearing trousers. What is the probability this student is a girl? The correct answer can be computed using Bayes' theorem.
The event A is that the student observed is a girl, and the event B is that the student observed is wearing trousers. To compute P(A|B), we first need to know:
P(B|A'), or the probability of the student wearing trousers given that the student is a boy. This is given as 1.
P(A), or the probability that the student is a girl regardless of any other information. Since the observers sees a random student, meaning that all students have the same probability of being observed, and the fraction of girls among the students is 40%, this probability equals 0.4.
P(A'), or the probability that the student is a boy regardless of any other information (A' is the complementary event to A). This is 60%, or 0.6.
P(B|A), or the probability of the student wearing trousers given that the student is a girl. As they are as likely to wear skirts as trousers, this is 0.5","In probability theory, Bayes' theorem (often called Bayes' law after Rev Thomas Bayes) relates the conditional and marginal probabilities of two random events. It is often used to compute posterior probabilities given observations. For example, a patient may be observed to have certain symptoms. Bayes' theorem can be used to compute the probability that a proposed diagnosis is correct, given that observation. (See example 2)
As a formal theorem, Bayes' theorem is valid in all common interpretations of probability. However, it plays a central role in the debate around the foundations of statistics: frequentist and Bayesian interpretations disagree about the ways in which probabilities should be assigned in applications. Frequentists assign probabilities to random events according to their frequencies of occurrence or to subsets of populations as proportions of the whole, while Bayesians describe probabilities in terms of beliefs and degrees of uncertainty. The articles on Bayesian probability and frequentist probability discuss these debates in greater detail.
Bayes' theorem relates the conditional and marginal probabilities of events A and B, where B has a non-vanishing probability:
P(A|B) = \frac{P(B | A)\, P(A)}{P(B)}.
Each term in Bayes' theorem has a conventional name:
* P(A) is the prior probability or marginal probability of A. It is ""prior"" in the sense that it does not take into account any information about B.
* P(A|B) is the conditional probability of A, given B. It is also called the posterior probability because it is derived from or depends upon the specified value of B.
* P(B|A) is the conditional probability of B given A.
* P(B) is the prior or marginal probability of B, and acts as a normalizing constant.
Intuitively, Bayes' theorem in this form describes the way in which one's beliefs about observing 'A' are updated by having observed 'B'.
","3"''', False)
doc3 = ('''"A Vector space model (or term vector model) is an algebraic way of representing text documents (and any objects, in general) as vectors of identifiers, such as index terms. It is used in information filtering, information retrieval, indexing and relevancy rankings. Its first application was in the SMART Information Retrieval System.
A document can be represented as a vector. Every dimension relates to a different term. If a term appears in the document, the terms value in the vector is non-zero. Many different methods of calculating these values, sometimes known as (term) weights, have been developed. tf-idf weighting is one of the most well known schemes. (see below example).
The definition of a term depends on the application. Normally a term is a single word, keyword, or a longer phrase. If the words are chosen to be the terms, the dimensionality of the vector is the number of words in the vocabulary (the number of distinct words occurring in the corpus).
The vector space model has some limitations:
1. Longer documents are represented poorly because the documents have poor similarity values (namely a small scalar product and a large dimensionality)
2. Search keywords have to precisely match document terms; word substrings could potentially result in a ""false positive match""
3. Semantic sensitivity; documents with a similar context, but different term vocabulary won't be associated, resulting in a ""false negative match"".
4. The order in which terms appear in the document is lost in a vector space representation.
","Vector space model (or term vector model) is an algebraic model for representing text documents (and any objects, in general) as vectors of identifiers, such as, for example, index terms. It is used in information filtering, information retrieval, indexing and relevancy rankings. Its first use was in the SMART Information Retrieval System.
A document is represented as a vector. Each dimension corresponds to a separate term. If a term occurs in the document, its value in the vector is non-zero. Several different ways of computing these values, also known as (term) weights, have been developed. One of the best known schemes is tf-idf weighting (see the example below).
The definition of term depends on the application. Typically terms are single words, keywords, or longer phrases. If the words are chosen to be the terms, the dimensionality of the vector is the number of words in the vocabulary (the number of distinct words occurring in the corpus).
The vector space model has the following limitations:
1. Long documents are poorly represented because they have poor similarity values (a small scalar product and a large dimensionality)
2. Search keywords must precisely match document terms; word substrings might result in a ""false positive match""
3. Semantic sensitivity; documents with similar context but different term vocabulary won't be associated, resulting in a ""false negative match"".
4. The order in which the terms appear in the document is lost in the vector space representation.''', True)
#The algorythm is very sensitive to words which contain in new text.
# docs = [doc1, doc2, doc3]
# v = IGTree(method=GAINRATIO)
# for document, t in docs:
# v.train(document, type=t)
#
# for type, message in (
# (True, doc1[0][:int(len(doc1[0])*0.5)]),
# (False, doc2[0][:int(len(doc2[0])*0.5)]),
# (True, doc3[0][:int(len(doc3[0])*0.5)])):
# print(v.classify(message))
#from pattern.db import csv, pd
#data = csv(pd("..", "..", "test", "corpora", "polarity-nl-bol.com.csv"))
#data = ((review, score) for score, review in data)
#
#print(kfoldcv(IGTree, data, folds=3))
#--- SINGLE-LAYER PERCEPTRON ------------------------------------------------------------------------
def softmax(p):
""" Returns a dict with float values that sum to 1.0
(using generalized logistic regression).
"""
if p:
v = list(p.values())
m = max(v)
e = list([exp(x - m) for x in v]) # prevent overflow
s = sum(e)
v = list([x / s for x in e])
p = defaultdict(float, list(zip(list(p.keys()), v)))
return p
#print(softmax({"cat": +1, "dog": -1})) # {"cat": 0.88, "dog": 0.12}
#print(softmax({"cat": +2, "dog": -2})) # {"cat": 0.98, "dog": 0.02}
class SLP(Classifier):
def __init__(self, train=[], baseline=MAJORITY, iterations=1, **kwargs):
""" Perceptron (SLP, single-layer averaged perceptron) is a simple artificial neural network,
a supervised learning method sometimes used for i.a. part-of-speech tagging.
Documents are classified based on the neuron that outputs the highest weight
for the given inputs (i.e., document vector features).
A feature is taken to occur in a vector (1) or not (0), i.e. BINARY weight.
"""
self._weight = defaultdict(dict) # {class: {feature: (weight, weight sum, timestamp)}}
self._iterations = iterations
self._iteration = 0
train = list(train)
train = chain(*(shuffled(train) for i in range(iterations)))
Classifier.__init__(self, train, baseline)
@property
def iterations(self):
return self._iterations
@property
def features(self):
return list(set(chain(*(list(f.keys()) for f in self._weight.values()))))
def train(self, document, type=None):
""" Trains the classifier with the given document of the given type (i.e., class).
A document can be a Document, Vector, dict, list or string.
If no type is given, Document.type will be used instead.
"""
def _update(type, feature, weight, i):
# Collins M. (2002). Discriminative Training Methods for Hidden Markov Models. EMNLP 2002.
# Based on: http://honnibal.wordpress.com/2013/09/11/
# Accumulate average weights (prevents overfitting).
# Instead of keeping all intermediate results and averaging them at the end,
# we keep a running sum and the iteration in which the sum was last modified.
w = self._weight[type]
w0, w1, j = w[feature] if feature in w else (random() * 2 - 1, 0, 0)
w0 += weight
w[feature] = (w0, (i - j) * w0 + w1, i)
type, vector = self._vector(document, type=type)
self._classes[type] = self._classes.get(type, 0) + 1
t1 = type
t2 = SLP.classify(self, document)
if t1 != t2: # Error correction.
self._iteration += 1
for f in vector:
_update(t1, f, +1, self._iteration)
_update(t2, f, -1, self._iteration)
def classify(self, document, discrete=True):
""" Returns the type with the highest probability for the given document.
If the classifier has been trained on LSA concept vectors
you need to supply LSA.transform(document).
"""
v = self._vector(document)[1]
i = self._iteration or 1
i = float(i)
p = defaultdict(float)
for type, w in self._weight.items():
#p[type] = sum(w[f][0] for f in v if f in w) # Without averaging.
s = 0.
for f in v:
if f in w:
w0, w1, j = w[f]
s += ((i - j) * w0 + w1) / i
p[type] = s
# Normalize probability estimates.
p = softmax(p)
#m = min(chain(p.values(), (0,)))
#s = sum(x-m for x in p.values()) or 1
#for type in p:
# p[type] -= m
# p[type] /= s
if not discrete:
return Probabilities(self, p)
try:
# Ties are broken in favor of the majority class
# (random winner for majority ties).
m = max(p.values())
p = sorted((self._classes[type], type) for type, w in p.items() if w == m > 0)
p = [type for frequency, type in p if frequency == p[0][0]]
return choice(p)
except:
return self.baseline
def finalize(self):
""" Removes training data from memory, keeping only the node weights,
reducing file size with Classifier.save().
"""
self._vectors = []
AP = AveragedPerceptron = Perceptron = SLP
# Perceptron learns one training example at a time,
# adjusting weights if the example is predicted wrong.
# Higher accuracy can be achieved by doing multiple iterations:
#from pattern.vector import Perceptron, shuffled
#
#p = Perceptron()
#for i in range(5):
# for v in shuffled(data):
# p.train(v)
#p = Perceptron()
#p.train({"woof":1, "howl":1}, "dog")
#p.train({"woof":1, "bark":1}, "dog")
#p.train({"woof":1, "bark":1}, "dog")
#p.train({"meow":1, "purr":1}, "cat")
#print p.features
#print p.classify({"woof":1, "bark":1}, discrete=False).max
#--- BACKPROPAGATION NEURAL NETWORK -----------------------------------------------------------------
# "Deep learning" refers to deep neural networks and deep belief systems.
# Deep neural networks are networks that have hidden layers between the input and output layers.
# By contrast, Perceptron directly feeds the input to the output layer.
# Weight initialization:
RANDOM = "random"
def matrix(m, n, a=0.0, b=0.0):
""" Returns an n x m matrix with values 0.0.
If a and b are given, values are uniformly random between a and b.
"""
if a == b == 0:
return [[0.0] * n for i in range(m)]
return [[uniform(a, b) for j in range(n)] for i in range(m)]
def sigmoid(x):
""" Forward propagation activation function.
"""
#return 1.0 / (1.0 + math.exp(-x))
return tanh(x)
def sigmoid_derivative(y):
""" Backward propagation activation function derivative.
"""
#return y * (1.0 - y)
return 1.0 - y * y
class BPNN(Classifier):
def __init__(self, train=[], baseline=MAJORITY, layers=2, iterations=1000, **kwargs):
""" Backpropagation neural network (BPNN) is a supervised learning method
bases on a network of interconnected neurons
inspired by an animal's nervous system (i.e., the brain).
"""
# Based on:
# http://www.cs.pomona.edu/classes/cs30/notes/cs030neural.py
# http://arctrix.com/nas/python/bpnn.py
self._layers = layers
self._iterations = iterations
self._rate = kwargs.get("rate", 0.5)
self._momentum = kwargs.get("momentum", 0.1)
self._trained = False
Classifier.__init__(self, train, baseline)
@property
def layers(self):
return self._layers
@property
def iterations(self):
return self._iterations
@property
def rate(self):
return self._rate
@property
def momentum(self):
return self._momentum
learningrate = learning_rate = rate
def _weight_initialization(self, i=1, o=1, hidden=1, method=RANDOM, a=0.0, b=1.0):
""" Initializes the network with the given number of input, hidden, output nodes.
Initializes the node weights uniformly random between a and b.
"""
i += 1 # bias
# Node activation.
self._ai = [1.0] * i
self._ao = [1.0] * o
self._ah = [1.0] * hidden
# Node weights (w) and recent change (c).
self._wi = matrix(i, hidden, a, b)
self._ci = matrix(i, hidden)
self._wo = matrix(hidden, o, a, b)
self._co = matrix(hidden, o)
def _propagate_forward(self, input=[]):
""" Propagates the input through the network and returns the output activiation.
"""
ai, ao, ah, wi, wo = self._ai, self._ao, self._ah, self._wi, self._wo
assert len(input) == len(ai) - 1
# Activate input nodes.
for i, v in enumerate(input):
ai[i] = v
# Activate hidden nodes.
for j, v in enumerate(ah):
ah[j] = sigmoid(sum((v * wi[i][j] for i, v in enumerate(ai))))
# Activate output nodes.
for k, v in enumerate(ao):
ao[k] = sigmoid(sum((v * wo[j][k] for j, v in enumerate(ah))))
return list(ao)
def _propagate_backward(self, output=[], rate=0.5, momentum=0.1):
""" Propagates the output through the network and
generates delta for hidden and output nodes.
The learning rate determines speed vs. accuracy of the algorithm.
"""
ai, ao, ah, wi, wo, ci, co = self._ai, self._ao, self._ah, self._wi, self._wo, self._ci, self._co
# Compute delta for output nodes.
do = [0.0] * len(ao)
for k, v in enumerate(ao):
error = output[k] - v
do[k] = error * sigmoid_derivative(v)
# Compute delta for hidden nodes.
dh = [0.0] * len(ah)
for j, v in enumerate(ah):
error = sum(do[k] * wo[j][k] for k in range(len(ao)))
dh[j] = error * sigmoid_derivative(v)
# Update output weights.
for j, v1 in enumerate(ah):
for k, v2 in enumerate(ao):
change = do[k] * v1
wo[j][k] += rate * change + momentum * co[j][k]
co[j][k] = change
# Update input weight.
for i, v1 in enumerate(ai):
for j, v2 in enumerate(ah):
change = dh[j] * v1
wi[i][j] += rate * change + momentum * ci[i][j]
ci[i][j] = change
# Compute and return error.
return sum(0.5 * (output[k] - v) ** 2 for k, v in enumerate(ao))
_backprop = _propagate_backward
def _train(self, data=[], iterations=1000, rate=0.5, momentum=0.1):
""" Trains the network with the given data using backpropagation.
The given data is a list of (input, output)-tuples,
where each input and output a list of values.
For example, to learn the XOR-function:
nn = BPNN()
nn._weight_initialization(2, 1, hidden=2)
nn._train([
([0,0], [0]),
([0,1], [1]),
([1,0], [1]),
([1,1], [0])
])
print(nn._classify([0,0]))
print(nn._classify([0,1]))
"""
# Error decreases with each iteration.
for i in range(iterations):
error = 0.0
for input, output in data:
self._propagate_forward(input)
error += self._propagate_backward(output, rate, momentum)
def _classify(self, input):
return self._propagate_forward(input)
def train(self, document, type=None):
""" Trains the classifier with the given document of the given type (i.e., class).
A document can be a Document, Vector, dict, list or string.
If no type is given, Document.type will be used instead.
"""
Classifier.train(self, document, type)
self._trained = False
def classify(self, document, discrete=True):
""" Returns the type with the highest probability for the given document.
If the classifier has been trained on LSA concept vectors
you need to supply LSA.transform(document).
"""
if not self._trained:
# Batch learning (we need to know the number of features in advance).
n = float(len(self.classes)) - 1
H1 = list(sorted(self.features))
H2 = dict((x, i / n) for i, x in enumerate(self.classes)) # Class => float hash (0.0-1.0).
H3 = dict((i / n, x) for i, x in enumerate(self.classes)) # Class reversed hash.
v = [([v.get(f, 0.0) for f in H1], [H2[type]]) for type, v in self._vectors]
self._h = (H1, H2, H3)
self._weight_initialization(i=len(H1), o=1, hidden=self._layers, a=0.0, b=1.0)
self._train(v, self._iterations, self._rate, self._momentum)
self._trained = True
H1, H2, H3 = self._h
v = self._vector(document)[1]
i = [v.get(f, 0.0) for f in H1]
o = self._classify(i)[0]
c = min(H3.keys(), key=lambda k: abs(k - o))
c = H3[c]
return c
def finalize(self):
""" Removes training data from memory, keeping only the node weights,
reducing file size with Classifier.save().
"""
self._vectors = []
MLP = ANN = NN = NeuralNetwork = BPNN
#nn = BPNN()
#nn._weight_initialization(2, 1, hidden=2)
#nn._train([
# ([0,0], [0]),
# ([0,1], [1]),
# ([1,0], [1]),
# ([1,1], [0])
#])
#print(nn._classify([0,0]))
#print(nn._classify([0,1]))
#print
#--- SUPPORT VECTOR MACHINE ------------------------------------------------------------------------
# Pattern comes bundled with LIBSVM 3.17:
# http://www.csie.ntu.edu.tw/~cjlin/libsvm/
#
# Compiled binaries for 32-bit and 64-bit Windows, Mac OS X and Ubuntu are included.
# If no binary works, SVM() raises an ImportError,
# and you will need to download and compile LIBSVM from source.
# If Mac OS X complains during compilation, rename -soname" to "-install_name" in libsvm/Makefile.
# If the binary is named "libsvm.so.2", strip the ".2".
# Put the binary (i.e., "libsvm.dll" or "libsvm.so") in pattern/vector/svm/.
# Windows binaries can be downloaded from:
# http://www.lfd.uci.edu/~gohlke/pythonlibs/#libsvm
# SVM extensions:
LIBSVM, LIBLINEAR = \
"libsvm", "liblinear"
# SVM type:
SVC = CLASSIFICATION = 0
SVR = REGRESSION = 3
SVO = DETECTION = 2 # One-class SVM: X belongs to the class or not?
# SVM kernels:
LINEAR = 0 # Straight line: u' * v
POLYNOMIAL = 1 # Curved line: (gamma * u' * v + coef0) ** degree
RADIAL = RBF = 2 # Curved path: exp(-gamma * |u-v| ** 2)
# SVM solvers:
LOGIT = L2LR = 0 # LIBLINEAR L2 logistic regression
# The simplest way to divide two clusters is a straight line.
# If the clusters are separated by a curved line,
# separation may be easier in higher dimensions (using a kernel).
class SVM(Classifier):
def __init__(self, *args, **kwargs):
""" Support Vector Machine (SVM) is a supervised learning method
where training documents are represented as points in n-dimensional space.
The SVM constructs a number of hyperplanes that subdivide the space.
Optional parameters:
- type = CLASSIFICATION,
- kernel = LINEAR,
- solver = 1,
- degree = 3,
- gamma = 1 / len(SVM.features),
- coeff0 = 0,
- cost = 1,
- epsilon = 0.01,
- cache = 100,
- shrinking = True,
- extension = (LIBSVM, LIBLINEAR),
- train = []
"""
from . import svm
self._svm = svm
# Cached LIBSVM or LIBLINEAR model:
self._model = None
# SVM.extensions is a tuple of extension modules that can be used.
# By default, LIBLINEAR will be used for linear SVC (it is faster).
# If you do not want to use LIBLINEAR, use SVM(extension=LIBSVM).
self._extensions = \
kwargs.get("extensions",
kwargs.get("extension", (LIBSVM, LIBLINEAR)))
# Optional parameters are read-only:
# - cost: higher cost = less margin for error (and risk of overfitting).
# - gamma: influence ("radius") of each training example for RBF.
if len(args) > 0:
kwargs.setdefault("train", args[0])
if len(args) > 1:
kwargs.setdefault("type", args[1])
if len(args) > 2:
kwargs.setdefault("kernel", args[2])
for k1, k2, v in (
( "type", "s", CLASSIFICATION),
( "kernel", "t", LINEAR),
( "solver", "f", 1), # For LIBLINEAR.
( "degree", "d", 3), # For POLYNOMIAL.
( "gamma", "g", 0), # For POLYNOMIAL + RADIAL.
( "coeff0", "r", 0), # For POLYNOMIAL.
( "cost", "c", 1), # Can be optimized with gridsearch().
( "epsilon", "p", 0.1),
( "nu", "n", 0.5),
( "cache", "m", 100), # MB
( "shrinking", "h", True)):
v = kwargs.get(k2, kwargs.get(k1, v))
setattr(self, "_" + k1, v)
# Type aliases.
if self._type == "svc":
self._type = SVC
if self._type == "svr":
self._type = SVR
if self._type == "svo":
self._type = SVO
# Kernel aliases.
if self._kernel == "rbf":
self._kernel = RBF
# Solver aliases.
if self._solver == "logit":
self._solver = LOGIT
Classifier.__init__(self, train=kwargs.get("train", []), baseline=MAJORITY)
@property
def extension(self):
""" Yields the extension module used (LIBSVM or LIBLINEAR).
"""
if LIBLINEAR in self._extensions and \
self._svm.LIBLINEAR and \
self._type == CLASSIFICATION and \
self._kernel == LINEAR:
return LIBLINEAR
return LIBSVM
@property
def _extension(self):
""" Yields the extension module object,
e.g., pattern/vector/svm/3.17/libsvm-mac64.so.
"""
if self.extension == LIBLINEAR:
return self._svm.liblinear.liblinear
return self._svm.libsvm.libsvm
@property
def type(self):
return self._type
@property
def kernel(self):
return self._kernel
@property
def solver(self):
return self._solver
@property
def degree(self):
return self._degree
@property
def gamma(self):
return self._gamma
@property
def coeff0(self):
return self._coeff0
@property
def cost(self):
return self._cost
@property
def epsilon(self):
return self._epsilon
@property
def nu(self):
return self._nu
@property
def cache(self):
return self._cache
@property
def shrinking(self):
return self._shrinking
s, t, d, g, r, c, p, n, m, h = (
type, kernel, degree, gamma, coeff0, cost, epsilon, nu, cache, shrinking
)
@property
def support_vectors(self):
""" Yields the support vectors.
"""
if self._model is None:
self._train()
if self.extension == LIBLINEAR:
return []
return self._model[0].get_SV()
sv = support_vectors
def _train(self):
""" Calls libsvm.svm_train() to create a model.
Vector classes and features are mapped to integers.
"""
# Note: LIBLINEAR feature indices start from 1 (not 0).
M = [v for type, v in self._vectors] # List of vectors.
H1 = dict((w, i + 1) for i, w in enumerate(self.features)) # Feature => integer hash.
H2 = dict((w, i + 1) for i, w in enumerate(self.classes)) # Class => integer hash.
H3 = dict((i + 1, w) for i, w in enumerate(self.classes)) # Class reversed hash.
x = list(map(lambda v: dict(map(lambda k: (H1[k], v[k]), v)), M)) # Hashed vectors.
y = list(map(lambda v: H2[v[0]], self._vectors)) # Hashed classes.
# For linear SVC, use LIBLINEAR which is faster.
# For kernel SVC, use LIBSVM.
if self.extension == LIBLINEAR:
f = self._svm.liblinearutil.train
o = "-s %s -c %s -p %s -q" % (
self._solver, # -f
self._cost, # -c
self._epsilon # -p
)
else:
f = self._svm.libsvmutil.svm_train
o = "-s %s -t %s -d %s -g %s -r %s -c %s -p %s -n %s -m %s -h %s -b %s -q" % (
self._type, # -s
self._kernel, # -t
self._degree, # -d
self._gamma, # -g
self._coeff0, # -r
self._cost, # -c
self._epsilon, # -p
self._nu, # -n
self._cache, # -m
int(self._shrinking), # -h
int(self._type != DETECTION), # -b
)
# Cache the model and the feature hash.
# SVM.train() will remove the cached model (since it needs to be retrained).
self._model = (f(y, x, o.split()), H1, H2, H3)
def _classify(self, document, probability=False):
""" Calls libsvm.svm_predict() with the cached model.
For CLASSIFICATION, returns the predicted class.
For CLASSIFICATION with probability=True, returns a {class: weight} dict.
For REGRESSION, returns a float.
"""
if self._model is None:
return None
M = self._model[0]
H1 = self._model[1]
H2 = self._model[2]
H3 = self._model[3]
n = len(H1)
v = self._vector(document)[1]
v = dict(map(lambda k: (H1.get(k[1], k[0] + n + 1), v[k[1]]), enumerate(v)))
s = getattr(self, "_solver", None)
# For linear SVC, use LIBLINEAR which is 10x faster.
# For kernel SVC, use LIBSVM.
if self.extension == LIBLINEAR:
f = self._svm.liblinearutil.predict
o = "-b %s -q" % (int(probability) if s == 0 else 0)
else:
f = self._svm.libsvmutil.svm_predict
o = "-b %s -q" % (int(probability))
p = f([0], [v], M, o)
# Note: LIBLINEAR only supports probabilities for logistic regression (solver=0).
if self._type == CLASSIFICATION and probability and self.extension == LIBLINEAR and s != 0:
return Probabilities(self, defaultdict(float, ((H3[p[0][0]], 1.0),)))
if self._type == CLASSIFICATION and probability and self.extension == LIBLINEAR:
return Probabilities(self, defaultdict(float, ((H3[i + 1], w) for i, w in enumerate(p[2][0]))))
if self._type == CLASSIFICATION and probability:
return Probabilities(self, defaultdict(float, ((H3[i + 0], w) for i, w in enumerate(p[2][0]))))
if self._type == CLASSIFICATION:
return H3.get(int(p[0][0]))
if self._type == REGRESSION:
return p[0][0]
if self._type == DETECTION:
return p[0][0] > 0 # -1 = outlier => return False
return p[0][0]
def train(self, document, type=None):
""" Trains the classifier with the given document of the given type (i.e., class).
A document can be a Document, Vector, dict, list or string.
If no type is given, Document.type will be used instead.
"""
Classifier.train(self, document, type)
self._model = None
def classify(self, document, discrete=True):
""" Returns the type with the highest probability for the given document.
If the classifier has been trained on LSA concept vectors
you need to supply LSA.transform(document).
"""
if self._model is None:
self._train()
return self._classify(document, probability=not discrete)
def save(self, path, final=False):
if self._model is None:
self._train()
if self.extension == LIBSVM:
self._svm.libsvmutil.svm_save_model(path, self._model[0])
if self.extension == LIBLINEAR:
self._svm.liblinearutil.save_model(path, self._model[0])
# Save LIBSVM/LIBLINEAR model as a string.
# Unlink LIBSVM/LIBLINEAR binaries for cPickle.
svm, model = self._svm, self._model
self._svm = None
self._model = (open(path, "rb").read(),) + model[1:]
Classifier.save(self, path, final)
self._svm = svm
self._model = model
@classmethod
def load(cls, path):
return Classifier.load(path)
def _on_load(self, path):
# Called from Classifier.load().
# The actual SVM model was stored as a string.
# 1) Import pattern.vector.svm.
# 2) Extract the model string and save it as a temporary file.
# 3) Use pattern.vector.svm's LIBSVM or LIBLINEAR to load the file.
# 4) Delete the temporary file.
from . import svm # 1
self._svm = svm
if self._model is not None:
f = tempfile.NamedTemporaryFile("r+b")
f.write(self._model[0]) # 2
f.seek(0)
if self.extension == LIBLINEAR and not svm.LIBLINEAR:
raise ImportError("can't import liblinear")
if self.extension == LIBLINEAR:
m = self._svm.liblinearutil.load_model(f.name)
if self.extension == LIBSVM:
m = self._svm.libsvmutil.svm_load_model(f.name)
self._model = (m,) + self._model[1:] # 3
f.close() # 4
def finalize(self):
""" Removes training data from memory, keeping only the LIBSVM/LIBLINEAR trained model,
reducing file size with Classifier.save() (e.g., 15MB => 3MB).
"""
if self._model is None:
self._train()
self._vectors = []
#---------------------------------------------------------------------------------------------------
# "Nothing beats SVM + character n-grams."
# Character n-grams seem to capture all information: morphology, context, frequency, ...
# SVM will discover the most informative features.
# Each row in the CSV is a score (positive = +1, negative = –1) and a Dutch book review.
# Can we learn from this dataset to predict sentiment? Yes we can!
# The following script demonstrates sentiment analysis for Dutch book reviews,
# with 90% accuracy, in 10 lines of Python code:
#from pattern.db import CSV
#from pattern.vector import SVM, chngrams, kfoldcv
#
#def v(s):
# return chngrams(s, n=4)
#
#data = CSV.load(os.path.join("..", "..", "test", "corpora", "polarity-nl-bol.com.csv"))
#data = map(lambda p, review: (v(review), int(p) > 0), data)
#
#print(kfoldcv(SVM, data, folds=3))
#---------------------------------------------------------------------------------------------------
# I hate to spoil your party..." by Lars Buitinck.
# As pointed out by Lars Buitinck, words + word-level bigrams with TF-IDF can beat the 90% boundary:
#from pattern.db import CSV
#from pattern.en import ngrams
#from pattern.vector import Model, SVM, gridsearch
#
#def v(s):
# return count(words(s) + ngrams(s, n=2))
#
#data = CSV.load(os.path.join("..", "..", "test", "corpora", "polarity-nl-bol.com.csv"))
#data = map(lambda p, review: Document(v(review), type=int(p) > 0), data)
#data = Model(data, weight="tf-idf")
#
#for p in gridsearch(SVM, data, c=[0.1, 1, 10], folds=3):
# print(p)
# This reports 92% accuracy for the best run (c=10).
# Of course, it's optimizing for the same cross-validation
# that it's testing on, so this is easy to overfit.
# In scikit-learn it will run faster (4 seconds <=> 20 seconds), see: http://goo.gl/YqlRa
#--- LOGISTIC REGRESSION ---------------------------------------------------------------------------
# Multinomial logistic regression (or Maximum Entropy) is competitive to SVM and gives probabilities.
class LR(Classifier):
def __init__(self, train=[], baseline=MAJORITY, iterations=100, **kwargs):
""" Logistic Regression is a supervised machine learning method,
Documents are assigned a probability for each possible class,
based on the probability that a feature occurs in a class
(independent of other features).
"""
import scipy
import scipy.sparse
import scipy.special
import scipy.optimize
self._iterations = iterations
self._model = None
Classifier.__init__(self, train, baseline)
@property
def iterations(self):
return self._iterations
def _train(self):
H1 = dict((w, i) for i, w in enumerate(self.classes)) # class => index
H2 = dict((i, w) for i, w in enumerate(self.classes)) # index => class
H3 = dict((w, i) for i, w in enumerate(self.features)) # feature => index
H4 = [w for w in sorted(H3, key=H3.get)] # feature by index
# Sparse matrix.
# A full matrix of 10,000 vectors, 100,000 features
# would take 1,000,000,000 x 8 = 7.5GB.
m = len(self._vectors)
n = len(H4)
try:
x = scipy.sparse.lil_matrix((m, n))
for i, (type, v) in enumerate(self._vectors):
for w in v:
x[i, H3[w]] = v[w]
x = scipy.sparse.csr_matrix(x) # faster dot
y = scipy.array([H1[type] for type, v in self._vectors])
t = self._gradient_descent(x, y, l=0.1, iterations=self._iterations)
except:
t = None
self._model = (t, H1, H2, H3, H4)
def _classify(self, document):
if self._model is None:
return None
t, H1, H2, H3, H4 = self._model
if t is not None:
v = self._vector(document)[1]
v = scipy.array([v.get(w, 0.0) for w in H4])
v = scipy.hstack([[1], v])
p = scipy.special.expit(scipy.dot(t, v)) # sigmoid
p = dict((H2[i], p) for i, p in enumerate(p))
return p
return {}
def _gradient_descent(self, x, y, l=0.1, iterations=100):
# The cost function computes the mean squared error J.
# The cost function represents the average amount of incorrect predictions.
# The gradient function is the derivative of the cost function.
# The gradient function determines the direction in which to optimize.
# Gradient descent then iteratively minimizes the cost (in max iterations).
# L2 regularization prevents overfitting by excluding unlikely feature values.
def log(z):
return scipy.log(z.clip(min=1e-10))
def cost(t, x, y, l=0.1):
# Cost function.
m = float(len(y))
h = scipy.special.expit(x.dot(t)) # sigmoid
J = 1 / m * (scipy.dot(-y, log(h)) - scipy.dot(1 - y, log(1 - h)))
L2 = l / m / 2 * sum(t[1:] ** 2)
return J + L2
def gradient(t, x, y, l=0.1):
# Cost function derivative.
m = float(len(y))
h = scipy.special.expit(x.dot(t)) # sigmoid
g = 1 / m * x.T.dot((h - y).T).T # dot(h-y, x)
L2 = l / m * scipy.insert(t[1:], 0, 0)
return scipy.transpose(g + L2)
# Gradient descent:
if x.shape[0] > 0:
m = x.shape[0]
n = x.shape[1]
t = scipy.zeros((max(y) + 1, n + 1))
x = scipy.sparse.hstack([scipy.ones((m, 1)), x])
y = scipy.array(y)
for i in range(max(y) + 1):
t0 = scipy.zeros((n + 1, 1))
t0 = scipy.transpose(t0)
t[i, :] = scipy.optimize.fmin_cg(
lambda t: cost(t, x, 0 + (y == i), l), t0,
lambda t: gradient(t, x, 0 + (y == i), l),
maxiter = iterations,
disp = 0)
return t # theta
return None
def train(self, document, type=None):
""" Trains the classifier with the given document of the given type (i.e., class).
A document can be a Document, Vector, dict, list or string.
If no type is given, Document.type will be used instead.
"""
Classifier.train(self, document, type)
self._model = None
def classify(self, document, discrete=True):
""" Returns the type with the highest probability for the given document.
If the classifier has been trained on LSA concept vectors
you need to supply LSA.transform(document).
"""
if self._model is None:
self._train()
p = self._classify(document)
if not discrete:
return Probabilities(self, p)
try:
# Ties are broken in favor of the majority class
# (random winner for majority ties).
m = max(p.values())
p = sorted((self._classes[type], type) for type, w in p.items() if w == m > 0)
p = [type for frequency, type in p if frequency == p[0][0]]
return choice(p)
except:
return self.baseline
def save(self, path, final=False):
if self._model is None:
self._train()
# Save array as string.
f = StringIO()
scipy.save(f, self._model[0])
f.seek(0)
self._model = (f.read(),) + self._model[1:]
Classifier.save(self, path, final)
@classmethod
def load(cls, path):
return Classifier.load(path)
def _on_load(self, path):
# Called from Classifier.load().
import scipy.sparse
import scipy.special
import scipy.optimize
if self._model is not None:
f = StringIO()
f.write(self._model[0])
f.seek(0)
self._model = (scipy.load(f),) + self._model[1:]
def finalize(self):
""" Removes training data from memory, keeping only the trained model (theta),
reducing file size with Classifier.save() (e.g., 15MB => 3MB).
"""
if self._model is None:
self._train()
self._vectors = []
LogisticRegression = SoftMaxRegression = MaximumEntropy = MaxEnt = ME = LR
#### GENETIC ALGORITHM #############################################################################
class GeneticAlgorithm(object):
def __init__(self, candidates=[], **kwargs):
""" A genetic algorithm is a stochastic search method based on natural selection.
Each generation, the fittest candidates are selected and recombined into a new generation.
With each new generation the system converges towards an optimal fitness.
"""
self.population = candidates
self.generation = 0
# Set GA.fitness(), crossover(), mutate() from function.
for f in ("fitness", "combine", "mutate"):
if f in kwargs:
setattr(self, f, types.MethodType(kwargs[f], self))
def fitness(self, candidate):
""" Must be implemented in a subclass, returns 0.0-1.0.
"""
return 1.0
def combine(self, candidate1, candidate2):
""" Must be implemented in a subclass, returns a new candidate.
"""
return None
def mutate(self, candidate):
""" Must be implemented in a subclass, returns a new candidate.
"""
return None or candidate
def update(self, top=0.5, mutation=0.5):
""" Updates the population by selecting the top fittest candidates,
and recombining them into a new generation.
"""
# 1) Selection.
# Choose the top fittest candidates.
# Including weaker candidates can be beneficial (diversity).
p = sorted(self.population, key=self.fitness, reverse=True)
p = p[:max(2, int(round(len(p) * top)))]
# 2) Reproduction.
# Choose random parents for crossover.
# Mutation avoids local optima by maintaining genetic diversity.
g = []
n = len(p)
for candidate in self.population:
i = randint(0, n - 1)
j = choice([x for x in range(n) if x != i]) if n > 1 else 0
g.append(self.combine(p[i], p[j]))
if random() <= mutation:
g[-1] = self.mutate(g[-1])
self.population = g
self.generation += 1
@property
def avg(self):
# Average fitness is supposed to increase each generation.
return float(sum(map(self.fitness, self.population))) / len(self.population)
average_fitness = avg
GA = GeneticAlgorithm